基于数据驱动战略的稳健和投机减少的不动产定价模型
Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy
摘要：
在许多国家，不动产评估是基于传统的方法，依靠评估人员的能力，收集数据，解释它和模型的价格不动产财产。随着不动产在线平台的使用越来越多，以及其中发现的大量信息，存在克服传统定价模型的许多缺点的可能性，例如主观性，成本，不公平性等。在本文中，我们提出了一个数据驱动的不动产定价模型的基础上，机器学习的方法来估计价格减少人类的偏见。我们使用2016年至2020年收集的波哥大178，865套公寓列表来测试该模型。结果表明，所提出的国家的最先进的模型是强大的和准确的估计不动产价格。这一案例研究激励发展中国家的地方政府根据大量数据集讨论和建立不动产定价模型，以提高对所有不动产市场利益攸关方的公平性，减少价格投机。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
