论文标题
基于数据驱动的策略建立强大和投机性的房地产定价模型
Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy
论文作者
论文摘要
在许多国家，房地产评估是基于常规方法，这些方法依靠评估者的能力收集数据，解释和建模房地产财产的价格。随着房地产在线平台的越来越多的使用以及其中的大量信息，存在可能克服许多常规定价模型的缺点，例如主观性，成本，不公平等。在本文中，我们提出了一个基于机器学习方法的数据驱动房地产定价模型，以估计价格降低人类偏见。我们使用波哥大的178,865个公寓清单测试了该模型，从2016年到2020年收集。结果表明，拟议的最新模型在估计房地产价格方面是强大而准确的。该案例研究是对发展中国家的地方政府讨论和建立房地产定价模型的激励措施，该模型基于大型数据集，从而增加了所有房地产市场利益相关者的公平性并减少了价格猜测。
In many countries, real estate appraisal is based on conventional methods that rely on appraisers' abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogotá, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.
