A decade of OCOD: analysing ten years of offshore-owned residential property in England and Wales

In the UK, and particularly London, property is a magnet for the global elite, drawn by both the glamour and the consistent rate of return of the housing market. Often, companies in tax or secrecy havens are used as the vehicle for these investments, making understanding this property class challenging. In 2015, the UK government began regularly releasing data on these offshore property investments. This paper analyses the first decade of this valuable but difficult-to-use dataset, first parsing and classifying the data into a structured format, then identifying the major residential trends. Over the last 10 years, the absolute value of residential OCOD property has increased from £64 to £80 billion; however, the relative value has decreased from 2.4 to 1.9 times the national average. London is the hub of offshore residential property, accounting for 45% of all properties by volume and 81% by value. However, these overall changes obscure more complex dynamics as luxury properties, owned by companies incorporated in the British Virgin Islands, decreased in value and volume across the time period. In contrast, there was an increase in housing developments being built by companies incorporated in Jersey and Guernsey. As Named Entity Recognition using deep learning is a critical part of the address parsing pipeline, we test different approaches and find that although training a model using weakly supervised learning (F1 = 0.97) marginally outperforms a model trained on a small, high-quality dataset (F1 = 0.95), this does not outweigh the cost of creating the labelling rules. As a result, we believe that investing more time in high-quality manual labelling is a better choice. The outputs of this paper are a pip-installable Python library and a pre-trained NER model that processes the datasets, increasing accessibility to this valuable resource for social and economic study.