回归模型在土地估价中的大规模评估应用:以越南中部广南省唐平区南会安项目c区为例

T. T. Phuong, Ho Viet Hoang, N. Việt, N. Ngoc, H. Chương
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引用次数: 0

摘要

本研究的重点是在广南省唐平区南会安项目C区临街和小巷土地定价系列中建立回归模型。本研究运用数据收集法、数据处理法、回归分析法等方法建立了大规模土地价值评估模型。本研究结果显示,南会安项目C区立面的位置受到安全性、地块宽度、地块形状、与会安城市的距离、交通拥堵、土地利用类型、道路边界等多种因素的影响。同时,巷道内土地单价受治安、巷道等级、地块形状、与会安市的距离、道路结构、交通拥堵、巷道位置等因素的影响。影响临街地价的最大因素是土地利用类型,回归系数为BLAND_USE_TYPE = 14,199。巷道土地单价为回归系数BALLEY_LOCATION = 10772的巷道类型。从回归模型对土地单价精度的验证结果与补偿土地单价的差异可以忽略不计,这完全符合确定接近市场价格的价格水平的要求。本文的研究结果表明,利用回归模型进行大规模评估在土地征收和土地财务管理中的实际适用性。
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MASS APPRAISAL APPLICATION FOR LAND VALUATION USING REGRESSION MODEL: A CASE STUDY IN THE C ZONE OF NAM HOI AN PROJECT, THANG BINH DISTRICT, QUANG NAM PROVINCE, CENTRAL VIETNAM
This study focuses on building a regression model in the series of land pricing for frontages and alleys in the C zone of Nam Hoi An project, Thang Binh district, Quang Nam province. The research applied data collection method, data processing method, regression analysis method to build a model of mass land valuation. The results of this study show that the location of the facade in the C zone of ​​Nam Hoi An project is affected by various factors such as security, plot width, plot shape, distance to Hoi An city, traffic congestion, land use type, and road boundary. Meanwhile, the unit price of land in the alley is influenced by factors such as security, alley level, plot shape, distance to Hoi An city, road structure, traffic congestion and alley location. The biggest factor affecting frontage land price is the type of land use with regression coefficient BLAND_USE_TYPE = 14,199. In terms of unit price of alley land, that is the type of alley with regression coefficient BALLEY_LOCATION = 10,772. The results of verification of the accuracy of the land unit price from the regression model to the compensation land unit price have a negligible difference, which is completely consistent with the requirement of determining the price level close to the market price. The results of this study show the practical applicability of mass appraisal for land valuation using a regression model in land acquisition and financial management of land.
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