中国房地产批量估价算法探索

发布时间：2018-06-17 18:20

本文选题：批量房产价格评估 + 多元回归分析　；参考：《兰州大学》2013年硕士论文

【摘要】：随着国家房产税政策的不断出台和完善,中国的房产税征收已经进入实质性阶段,而作为税基的房产价格成为税收的关键,采用目前人工的评估方法去完成整个城市的房产评估几乎是不可能的,中国需要类似于西方国家CAMA(计算机辅助批量评估系统)系统的批量评估系统。本文详细介绍了CAMA系统的核心算法——多元回归分析在批量房地产估价中的应用,阐述了多元回分析归算法在目前中国批量房地产估价中的优劣性。同时本文提出的粒子群算法是对当前中国批量房地产估价系统算法的探索,它可以有效避免目前数据资料不足的缺点完成评估工作,是CAMA系统建立之初的临时性解决方案,与此同时它可以为CAMA系统积累原始数据资料,帮助尽快建立中国的CAMA系统。文章用MATLAB实现了此粒子群算法,对兰州市的若干处房地产实例进行评估,评估结果显示粒子群算法能够比较准确的完成少量样本数据条件下的房产评估。虽然此方法还存在一定的局限性,但是应用时间序列分析技术后粒子群算法的局限性可以被大大减小。所以说,采用粒子群算法作为现阶段的批量房产评估系统核心算法是切实可行的。
[Abstract]:With the continuous introduction and improvement of the national property tax policy, the collection of real estate tax in China has entered a substantial stage, and the price of real estate as the tax base has become the key to taxation. It is almost impossible to use the present artificial evaluation method to complete the real estate evaluation of the whole city. China needs a batch evaluation system similar to the CAMA (Computer-Aided batch Evaluation system) system in western countries. In this paper, the application of multivariate regression analysis, the core algorithm of CAMA system, in batch real estate evaluation is introduced in detail, and the advantages and disadvantages of multivariate return analysis algorithm in batch real estate evaluation in China are expounded. At the same time, the particle swarm optimization algorithm proposed in this paper is an exploration of the current batch real estate valuation system algorithm in China. It can effectively avoid the shortcomings of the current data shortage to complete the evaluation work. It is also a temporary solution to the CAMA system at the beginning of its establishment. At the same time, it can accumulate raw data for CAMA system and help establish CAMA system in China as soon as possible. In this paper, the particle swarm optimization (PSO) algorithm is implemented with MATLAB to evaluate some real estate cases in Lanzhou City. The results show that PSO algorithm can accurately complete the real estate evaluation under the condition of a small number of sample data. Although this method has some limitations, the limitation of PSO can be greatly reduced by using time series analysis. Therefore, it is feasible to use particle swarm optimization (PSO) as the core algorithm of batch real estate evaluation system.
【学位授予单位】：兰州大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP18;F299.23

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