Analyzing real estate transaction and pricing data via statistical machine learning

通过统计机器学习分析房地产交易和定价数据

• 批准号: 479555-2015
• 负责人: Niu, Di
• 金额: $ 4.37万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618879
• 关键词: Analyzing; real; estate; transaction; pricing

Real-estate property price estimation and prediction is key to Canadian home buyers, investors and policy makers. To date, most real-estate market assessments accessible by Canadian users are based on big trend analysis, qualitative prediction or traditional real-estate market indices, leading to a limited accuracy. The research literature has presented a large number of both parametric and non-parametric models to assess the market value of an individual property based on the sold prices of other properties transacted in the same region. However, even with non-parametric models and the latest kernel-based regression, existing approaches have limited accuracy and are unable to handle irregular regions with interior uninhabitated areas or to capture price jumps across heterogeneous areas. To overcome these difficulties, in this project, we will propose and extensively study a number of new statistical learning tools for applications to real-estate valuation, price estimation and prediction, including finite element analysis, spatial spline regression, roughness and fused-LASSO-type regularization, low-rank matrix completion and state-space models with penalized Kalman filters. With carefully designed structures, our proposed models can be decomposed and solved using convex optimization. We will also study the implementation issues for faster computation in commercial real-estate market assessment applications with a large amount of data.

房地产房地产价格估计和预测是加拿大购房者，投资者和政策制定者的关键。迄今为止，加拿大用户可访问的大多数房地产市场评估都是基于大趋势分析，定性预测或传统的房地产市场指数，从而导致准确性有限。研究文献提出了大量的参数模型和非参数模型，以根据同一地区的其他物业的出售价格来评估个人财产的市场价值。但是，即使使用非参数模型和最新的基于内核的回归，现有方法的准确性也有限，并且无法处理具有室内无人居区域的不规则区域，或者无法捕获异质区域的价格上涨。为了克服这些困难，在这个项目中，我们将提出并广泛研究用于实现房地产估值，价格估计和预测应用的许多新的统计学习工具，包括有限元分析，空间样条回归，粗糙度和融合熔融型 - 型号正规化，较低的零件矩阵完整和状态空间模型，并具有惩罚性的Kalman。使用精心设计的结构，我们提出的模型可以使用凸优化分解和解决。我们还将研究具有大量数据的商业房地产市场评估应用中更快计算的实施问题。