We combine economics, housing theory and data science to gain a greater understanding of low-use properties in England and Wales. We collect a unique dataset of domestic properties unoccupied by a permanent resident from 112 local authorities via freedom of information requests. The dataset covers 23 million residents and 340,000 low-use properties (3.4% of all properties). We find that the distribution is very skewed, with 5% of the lower super output areas (our smallest geographic unit) containing 29% of all low-use properties. We estimate the value of low-use properties in the dataset to be £123 billion and that an empty homes tax of 1% would generate the equivalent to 11% of the current council tax (local government tax). We use logistic regression to identify local authorities with high numbers of low-use properties (72% accuracy), local authorities where low-use properties are more expensive than ordinary homes (77% accuracy), and local authorities where both those conditions are true (79% accuracy). The coefficients of the models indicate that low-use property tends to be found in the most and least affordable areas and that the probability of low-use property being more expensive than a regular home increases as affordability decreases and tourism increases. We estimate that 39–47% of the population in England and Wales live in an area where low-use property is more expensive than property occupied by a full-time resident. We conclude that as the areas with the least affordable housing also tend to have the highest demand for low-use property, it may be appropriate to reduce demand via measures such as an empty homes tax rather than increasing housing supply.
The UK, particularly London, is a global hub for money laundering, a significant portion of which takes place through residential property. However, understanding the distribution and characteristics of offshore residential property in the UK is a challenge. 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 on 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 residential (95k), with a disproportionate number of those in London (42k). The average offshore residential property in London is worth 1.33 million GBP, and collectively this amounts to approximately 56 billion GBP. We perform an in-depth analysis of offshore residential property in London, comparing the price, distribution and entropy/concentration with Airbnb property, low-use/empty property and conventional residential property. We estimate that the total number of offshore, low-use and Airbnb properties in London is between 144,000 and 164,000, collectively worth between 145–174 billion GBP. Furthermore, offshore residential 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 encourage further research into this topic.
This paper introduces the strain elevation tension spring embedding (SETSe) algorithm. SETSe is a novel graph embedding method that uses a physical model to project feature-rich networks onto a manifold with semi-Euclidean properties. Due to its method, SETSe avoids the tractability issues faced by traditional force-directed graphs, having an iteration time and memory complexity that is linear to the number of edges in the network. SETSe is unusual as an embedding method as it does not reduce dimensionality or explicitly attempt to place similar nodes close together in the embedded space. Despite this, the algorithm outperforms five common graph embedding algorithms, on graph classification and node classification tasks, in low-dimensional space. The algorithm is also used to embed 100 social networks ranging in size from 700 to over 40,000 nodes and up to 1.5 million edges. The social network embeddings show that SETSe provides a more expressive alternative to the popular assortativity metric and that even on large complex networks, SETSe’s classification ability outperforms the naive baseline and the other embedding methods in low-dimensional representation. SETSe is a fast and flexible unsupervised embedding algorithm that integrates node attributes and graph topology to produce interpretable results.
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