论文标题
自助洗衣店有什么?在伦敦的海上拥有的国内财产的映射和特征
What's in the laundromat? Mapping and characterising offshore owned domestic property in London
论文作者
论文摘要
英国,尤其是伦敦,是洗钱的全球枢纽,其中很大一部分使用国内财产。但是,由于数据可用性,了解英国海上国内财产的分布和特征是具有挑战性的。本文试图通过增强海上公司拥有的英国财产的公开数据集来纠正这种情况。我们创建一个数据处理管道,该管道利用了几个数据集和机器学习技术,以创建一组被分类为六个使用类的地址集。增强的数据集比原始数据集多于138,000个属性。大多数是国内(95K),伦敦的人数不成比例。伦敦的平均海上国内财产价值133万英镑,总计约560亿英镑。我们对伦敦的海上国内财产进行了深入的分析,将价格,分配和熵/集中度与Airbnb财产,低使用/空财产和常规家庭财产进行了比较。我们估计,伦敦的离岸,低使用和Airbnb物业的总数在144,000至164,000之间,并且统称为145-1740亿英镑。此外,与所有其他财产类型相比,离岸国内财产更昂贵,并且具有更高的熵/集中度。此外,我们确定了具有不同价格和分销特征的两种不同类型的离岸属性,即嵌套和个体。最后,我们发布了增强的海上属性数据集,完整的低使用伦敦数据集以及创建增强数据集以减少研究该主题的障碍的管道。
The UK, particularly London, is a global hub for money laundering, a significant portion of which uses domestic property. However, understanding the distribution and characteristics of offshore domestic property in the UK is challenging due to data availability. This paper attempts to remedy that situation by enhancing a publicly available dataset of UK property owned by offshore companies. We create a data processing pipeline which draws on several datasets and machine learning techniques to create a parsed set of addresses classified into six use classes. The enhanced dataset contains 138,000 properties 44,000 more than the original dataset. The majority are domestic (95k), with a disproportionate amount of those in London (42k). The average offshore domestic property in London is worth 1.33 million GBP collectively this amounts to approximately 56 Billion GBP. We perform an in-depth analysis of the offshore domestic property in London, comparing the price, distribution and entropy/concentration with Airbnb property, low-use/empty property and conventional domestic property. We estimate that the total amount of offshore, low-use and airbnb property in London is between 144,000 and 164,000 and that they are collectively worth between 145-174 billion GBP. Furthermore, offshore domestic property is more expensive and has higher entropy/concentration than all other property types. In addition, we identify two different types of offshore property, nested and individual, which have different price and distribution characteristics. Finally, we release the enhanced offshore property dataset, the complete low-use London dataset and the pipeline for creating the enhanced dataset to reduce the barriers to studying this topic.