A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units
A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units
复制标题
DOI：
10.1061/(asce)co.1943-7862.0001047
复制
发表时间：
2016-02-01
影响因子：
5.1
通讯作者：
Adeli, Hojjat
中科院分区：
文献类型：
--
作者：
Rafiei, Mohammad Hossein;Adeli, Hojjat
预测住房价格对于任何国家的近期经济预测都至关重要。本文提出了一种新颖而全面的模型，通过深度置信受限玻尔兹曼机和独特的非配对遗传算法的巧妙集成，在设计阶段或施工开始时估算任何特定城市的新住房价格。建筑公司可以使用该模型在开始新建筑并考虑建造或不建造之前评估销售市场。提出了一种有效的数据结构，考虑了大量的经济变量/指数。该模型结合了变量的时间依赖性和季节性变化。人们已经开发出巧妙的策略来克服维数诅咒，并使问题的解决方案能够在标准工作站上进行。提出了一个案例研究来证明该模型的有效性和准确性。 (C) 2015 年美国土木工程师学会。
Predicting the price of housing is of paramount importance for near-term economic forecasting of any nation. This paper presents a novel and comprehensive model for estimating the price of new housing in any given city at the design phase or beginning of the construction through ingenious integration of a deep belief restricted Boltzmann machine and a unique nonmating genetic algorithm. The model can be used by construction companies to gauge the sale market before they start a new construction and consider to build or not to build. An effective data structure is presented that takes into account a large number of economic variables/indices. The model incorporates time-dependent and seasonal variations of the variables. Clever stratagems have been developed to overcome the dimensionality curse and make the solution of the problem amenable on standard workstations. A case study is presented to demonstrate the effectiveness and accuracy of the model. (C) 2015 American Society of Civil Engineers.