# What’s in the laundromat? Mapping and characterising offshore owned domestic
property in London

Jonathan Bourne corresponding author<EMAIL_ADDRESS>University
College London, The Bartlett Faculty of the Built Environment  Andrea Ingianni
Kingston University Department of Economics  Rex McKenzie  Kingston University
Department of Economics

###### Abstract

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.

##  1 Introduction

The UK, and in particular London, is hub for international money laundering
using property [1]. The volumes of money laundered through the UK capital are
believed to be so high the city is sometimes refereed to as ‘The London
Laundromat’. A substantial part of the reason for this is that in the UK it is
possible for offshore shell companies to purchase property. This allows those
with illicit incomes to buy property without being identified [2]. In the
‘National risk assessment of money laundering and terrorist financing 2020’
The risk of money laundering using property purchases was rated as high [3],
with domestic property being a particular focus due to the large amounts of
money that can be moved in a single transaction and the lifestyle benefits
such properties bring. In addition transparency international have found that
75% of money laundering cases involving property use offshore shell companies
[4]. However, attempts to control money laundering have struggled for decades
[5], for an in depth discussion of money laundering and property see [6, 7] In
2015 a list of offshore owned properties in England and Wales was leaked to,
and made public by, the ‘Private Eye’ a British publication [8]. Since then
the UK government has published a regularly updated dataset of offshore owned
properties called the ‘Overseas companies that own property in England and
Wales - Use land and property data’ (OCOD) [9].

The OCOD dataset has the potential to be a great resource for those wishing to
understand a the distribution and characteristics of offshore owned property
in the UK. However, although the dataset has been used by researchers [10,
11], the dataset is challenging to use with addresses recorded as free text.
These addresses can be incomplete, contain nested properties, where multiple
properties exists within a single row or title number, it also contains no
information on whether the property is domestic, business or something else.
Such poor quality data makes understanding the distribution and
characteristics of offshore owned property in the UK challenging.

In addition to offshore owned domestic property, London has large amounts of
short term lets and low-use/empty property. In this paper the Offshore
domestic property, airbnb property and low-use property, will be grouped under
the term ‘Unconventional Domestic Property’ (UDP). Short term lets of the sort
facilitated by Airbnb, One Fine Stay and Vrbo have recieved a quantiative
exploration In both London and other major cities [12, 13, 14]. Low-use
property, i.e. property with no permanent resident, has also received coverage
[15, 16, 17], but less than the short term lets market due to difficulty
obtaining data which is seldom publicly available due to privacy concerns. In
the UK data gathering is further complicated by the distinct sub-national
jurisdictions. For example, the register of property sales is public in
England and Wales [18] but not in Scotland. To counter some of the
difficulties with property data the UK government developed the Unique
Property Reference Number (UPRN) [19]. The UPRN is a referencing system
designed increase the ease of linking property data across government
datasets. However, whilst the use of the UPRN is mandated, almost no
government department uses it meaning linking the data requires advanced data
processing skills [20, 21].

A partial solution to the data issues within OCOD and other property datasets,
is the use of address parsers. Recently opensource address parsers have been
developed to convert free text addresses to a tabular and normalised format,
which is much easier to analyse [22, 23, 24]. Address parsing is a form of
Named Entity Recognition (NER) which is a subfield of Natural Language
Processing (NLP) which is itself a subfield of Machine Learning (ML). NER uses
statistical techniques to identify certain words within a sentence or larger
block of text which represent a predefined entity and labels them with a given
entity class. For address data, an example of a specific class could be
‘street name’ or ‘postcode’. The named entity is defined by its ‘Span’ which
is the position number of the start and end characters as well as the label of
the entity class.

Although very valuable the existing address parser’s are not without problems.
Many of the parsers are are trained using Open Street map data [25], this has
the advantage that there are many millions of stored addresses to use as
training data, and algorithms can be trained to parse addresses from countries
across the world using multiple languages and scripts. However, it also means
that even though training data has noise induced to increase robustness,
competing algorithms are all trained on the same dataset. This training
dataset may be subtly different from the actual data the resulting algorithms
are applied to. This can lead to weaknesses, for example the most well
established parser libpostal [23] has been shown to have issues parsing
incomplete addresses [22], it also struggles with addresses in formats it is
not used to [26]. A more fundemental issue may be that the algorithms are
designed on the assumption that they are parsing a single address, the OCOD
dataset contains large numbers of nested addresses where a single free text
line may contain tens or even hundreds of properties. As such a custom parser
must be created to deal with the issues presented by this specific dataset.
Given that there is no real training data creating such a parser will need to
take advantage of recent developments in ML and NLP.

A major difficulty in any machine learning project is the obtaining of
labelled data. This is because the majority of machine learning tasks use
supervised learning. In supervised learning, the algorithm is instructed to
learn a specific task such as classification or regression, for which the
training data supplies the correct answer, the algorithm is considered trained
when the error between the predictions and ground truth is minimised. The data
used in supervised classification tasks typically has to be labelled by a
human. This is time consuming, and can be impractical for small teams or those
without funding to pay others using ‘crowd sourcing’ [27, 28]. A developing
solution to this issue is ‘Weak learning’ [29], in this approach data is
labelled using simple rules, this can produce a large number of labels which
are often correct, but can also overlap and contradict one-another. A
denoising process is then applied to the data using some statistical method
such as a hidden markov model [30] or matrix completion [31]. The denoising
process removes overlapping labels to minimise the error with the ground
truth. An obvious critique of weak learning is that the true labels are not
known bringing prediction quality into question, however one of the most
powerful aspects of weak learning is that it can produce models that are
almost equal in performance to hand labelled data [32], indeed the quality of
the model scales according the the size of the unlabelled dataset [29]. In
addition recent research has highlighted that most datasets have errors even
those used as a benchmark machine learning datasets such as MNIST, ImageNet
and Amazon reviews [33]; this suggests that datasets created either through
weak learning or hand labelling are on an error scale but can both produce
high quality models.

This paper proposes a new method of identifying and classifying offshore owned
property in England and Wales and uses this method to answer the research
question; How is offshore domestic property in London characterised in terms
of financial value and spatial distribution? How does this differ from low-use
property, airbnb property and conventional homes? Does offshore domestic
property appear to be broadly homogeneous, or does it contain distinct sub-
groups?

##  2 Method

The method is broken into three parts. The first part introduces the data, the
second part details how the OCOD dataset is cleaned and transformed into the
enhanced OCOD dataset. The third part describes the steps used in the
analysis.

It should be noted that although address parsing is a major part of this
project, the goal is not to create an universal address parser. As such the
focus of the method is the process of creating the enhanced OCOD dataset and
the resulting analysis.

In this project geographical analysis is performed on discrete units. These
units were created by the Office for National Statistics (ONS) for use in
census geography [34]. We use 3 levels of census geography which are (from
smallest to largest) the Output Area (OA), Lower Super Output Area (LSOA) and
Middle Super Output Area (MSOA), In addition we use the Local Authority
District (LAD), which is the smallest level of local government. On average a
LAD contains 30 MSOA although there can be substantial variance. The use of
the OA system provides a standard interface with government data and prevents
data privacy issues.

###  2.1 Data and code

This work uses several publicly available datasets. They key data set is the
‘Overseas companies that own property in England and Wales - Use land and
property data’ (OCOD) [9]. The data cleaning process uses the Land Registry
Price Paid dataset [18], The VOA ratings listing dataset [35] and the Office
of National Statistics Postcode Directory (ONSPD) [36]. The Airbnb data is
from the website InsideAirbnb [37], The airbnb properties in this dataset only
include entire homes which are let to the extent it is not possible to have a
permenent tennent. The low-use dataset uses data gathered using Freedom of
information (FOI) requests, the majority of which was collected for [15], but
the outstanding London boroughs have since been added using the same FOI
process (see github repo lsoa foi template maker). The definition of low-use
is the same as [15] which is is broadly that the property has no permanent
resident, but this is through choice. Price data used in the inference and
analysis section comes from the Price Paid dataset. The London Low-use dataset
and the Enhanced Offshore ownership dataset are both available along with the
code used at the github repository for this project JonnoB empty homes london.
The data description of the Enhanced OCOD can be found in supplementary
material.

It should be noted that the nested properties highlight another nuance with
the OCOD dataset. Nested properties are part of a title that is owned by an
offshore company but that title may refer to the freehold as apposed to the
lease hold. This means that the right to live in the property has been sold to
an individual for some period of time (e.g. 100 years) although the property
still belongs to the freeholder. Leaseholders usually pay ‘ground rent’ to the
freeholder. This leaseholds are typically case with flats and apartments. We
included both freehold and leasehold properties on the basis that in either
case the benefit either in terms of sale or ’ground rent’ asset value belongs
to the offshore company.

###  2.2 Creating the Enhanced OCOD dataset

The enhanced OCOD dataset is created by putting the original OCOD dataset
through a process of cleaning, labelling, parsing and classification. The end
result is a dataset that can be easily analysed and follows tidy data
principles [38] in that each property is an observation/row in the data. The
OCOD dataset is a CSV file, it contains several columns however for the
purpose of this paper two are of importance, the title number and the property
address. The title number is the unique reference given to each ‘property’
although in reality a title can contain many properties or none. The property
address contains the free text which will be parsed. The data cleaning and
preparation process is broken into five sections, Label, Parse, Expand,
Classify and Contract, in addition quality testing of the labelling and
classification is performed. A schematic of the process is shown in figure 1.

Figure 1: A schematic of the data cleaning process, from loading the OCOD
dataset to ouputting the final dataset ready for analysis

The address data has a form similar to the three examples below. As the data
is all addresses there is a degree of structure and predictability to it. The
addresses are broken into comma separated parts; e.g. if present, street
number or unit ID is always first, and postcode is always last, (see table 1).

Example | Address
---|---
1 | flat 6, chartfield house, babel road, london
2 | 5 to 15 (odds only) babel road, london (w1 8ap)
3 | 5 babel road, london and parking 3.5 w1 8ap
Table 1: Example address formats

After loading the OCOD dataset light pre-processing was performed such as
ensuring a space after comma and removing double spaces, changing to lower
case etc. The data was then imported to programmatic [39] where entities were
labelled using regex (Regular expressions) functions. There were around 50
functions used to describe the 8 types of named entity (Regex functions can be
found here
https://github.com/JonnoB/empty_homes_london/tree/main/humanloop_1_2/export).
These entities are unit ID, unit type, building name, street number, street
name, city, postcode, filter type. The entity classes are described in table
2.

Entity class |  Description
---|---
Unit ID |  Describes a sub-unit such as a flat number or parking space ID. Example one would have ‘6’ and example three would have ‘3.5’ as unit id. Unit Id is not always a number
Unit type |  Describes the type of unit if available. Example one would have ‘flat’ whilst example three would have "parking"
Building Name |  Example one would have ‘chartfield house’, the field would not be present for the other two examples
Street Number |  The street number of the property if available, would be ‘5 to 15’ in example two and ‘5’ in example three. Street number is not always a number
Street Name |  Self explanatory, would be ‘babel road’ in all three examples
Filter Type |  When multiple properties are included in the address a filtering condition is often used, this is because in the UK odd and even numbers are often on opposite sides of the road. Or a company may not own all the flats in an apartment block. Example two would have ‘odd’
City |  Self explanatory, would be London for all three examples
Postcode |  Self explanatory. In almost all cases the post code is in parenthesis. In addition UK postcodes follow a pattern which can be extracted using regex, making them easy to label
Table 2: Definitions of the entity classes used in the labelling process,
example formats are found in table 1.

Once the labels have been added to the data the whole dataset is extracted as
a json file. In some cases label spans overlap these are removed either using
a simple rule that keeps only the largest span, or a denoising process that
uses a hidden markov model, following the principles of weak labelling. In
addition, using the output of Programmatic, we create a predictive model using
spaCy [40], the model uses RoBERTa [41] as the underlying algorithm. RoBERTa
is a type of transformer [42], currently one of the most successful approaches
to solving problems in NLP.

Once the labels have been denoised a ground truth data set is hand labelled
using 1000 randomly sampled observations. An additional dev set of 2000
examples is also labelled using the same process. This ground truth dataset is
used to asses the quality of the NER labelling, using programmatic and spaCy.

####  2.2.1 Address parsing

With a single address the parsing process is effectively over when the the
entities have been labelled. However, in the case when there are multiple
properties the parsing process is more involved the label dependency becomes
important. As an example consider the nested address ’flat 5, chartfield house
and flat 16, zebra house, babel road, london (w1 8ap)’. In this example there
are two flats in two separate buildings on a single street and with a common
postcode, as such there are two distinct addresses with some shared labels. We
parsed the addresses by assigning each character span its own row and each
label class its own column. We then propergated the columns backwards to
create complete address rows. Certain labels were classed as terminators and
all non-terminator rows were removed from the data. Terminators were
hierarchically structured such that if the most high priority label class was
not available the next label class in the hierarchy was used. The terminator
list in hierarchical order is unit type, unit ID’s, building name, street
number. The presence of a span in a label column would block the backfilling
of data from later in the sentence, this prevented situations where houses
would be assigned the wrong road. When parsing was complete and address rows
without terminators were removed and a tabular dataset of parsed addresses was
returned.

As mentioned earlier and shown in table 1, some title numbers contained
multiple addresses using an abbreviated format. To have a single address per
line required that the dataset be expanded.

####  2.2.2 Address Expansion

Expanding the dataset so that each row represents a single property required
machine reading of the unit ID label and the filter number label. A regex
expression was used to identify unit ID’s or street numbers that covered
multiple addresses the first and last numbers were then extracted and the
numbers between them filled in making a contiguous list of numbers from i to
j. If a filter number label was present it was used to identify whether the
numbers were odd or even and the list of numbers were filtered to so only the
appropriate number type remained. With the data now parsed and expanded such
that a single property address was represented on a single line the data could
now be localised to a specific OA or LSOA and classified into a specific
property type.

####  2.2.3 Address Classification

The classification phase of the cleaning process includes classifying the
property types but also includes localising the properties within the UK
governments geographical area system. Initially all postcodes are cross
referenced with the Office of national Statistics’ Postcode Database (ONSPD).
This links the postcode and the OA system used by the ONS for census and other
demographic analysis. However, not all addresses have a postcode, in these
cases the parsed addresses are cross referenced with the land registry price
paid dataset and the VOA company ratings list dataset, if a match is found the
OA and LSOA is added to the OCOD address. For nested addresses the same OA and
LSOA is added to all addresses within the property title. The data is
classified into one of six classes, in a sequential process (see figure 2).
The 6 classification groups are airspace, business, domestic, land, parking
and ‘unknown’. There are two classification processes Type 1 which has twelve
classification steps, where each step identifies some aspect of a single
property class, (for details on the process see
classifying_the_ocod_dataset.ipynb, section ‘Classify Property type’). Any
properties which cannot be identified are classed as ‘unknown’, these
properties are almost entirely a mix of domestic and business. Once the Type 1
classification process is complete the Type two process is applied. As shown
in Figure 2 the Type two process builds on the output of Type 1 and deduces
whether a property is domestic or business by finding which addresses can be
identified. Type 2 has several sequential classification steps which filter
for the level of completeness of an address. When class deduction is possible
the Type two process changes the ‘unknown’ class of the Type one process to
‘domestic’ or ‘business’ as appropriate. However addresses which do not
contain enough information to accurately locate remain in the ‘unknown’ class.
The outputs of the processes are provided separately in the dataset as the
Type 1 process is more secure as it primarily relies on positively identifying
classes whereas Type two uses deduction to work out what the property is not.
The analysis will use Type two classification.

Figure 2: The classification process during the preparation of the enhanced
OCOD dataset.

####  2.2.4 Address Contraction

This is the final and most straight forward of all the cleaning steps. As it
is not unusual for commercial premises to occupy more than one ‘street
address’ non-domestic properties that are within a nested property group are
reduced back to single address. As such only domestic properties are ever
within a nested property title.

####  2.2.5 Test set and Accuracy metrics

Whilst the previous sections have described the steps by which the data is
prepared, they do not describe any way to measure the quality of the labelling
or classification processes. This is essential as the for the final analysis
to be considered reasonable the data needs to be of high quality. The data is
tested by randomly sampling 1000 addresses from the original dataset these
samples are then hand labelled and held out from any further analysis until
the end of the cleaning process. Figure 1 shows that the ground truth is
created after the initial labelling, this is for practical reasons to speed up
the hand labelling process as many of the labels will already be complete and
correct. Finally the classification quality is assessed. The manual checking
of the class is done by looking up the address using map software, looking at
pictures of the property, and doing an internet search of the address to see
if it is a domestic or a commercial property. This process is certainly not
infallible but without the use of the UPRN system there is no other way within
a reasonable time frame.

Once the ground truth has been created, the datasets produced by the processes
in this paper and alternative parsing algorithms can be compared. Performance
will be measured using the precision, recall and F1 metrics. Precision is the
probability of a span exactly matching the ground truth test set. In the
binary class case precision is defined as

$\mathrm{Precision}=\frac{tp}{tp+fp},$ (1)

where $tp$ is the total number of true positives where the span matches the
test set and $fp$ is total number of false positives where span does not.
Recall is the fraction of matched spans which are also given the correct
class, it is defined by

$\mathrm{Recall}=\frac{tp}{tp+fn},$ (2)

where $fn$ is the total number of false negatives. The F1 score is

$\mathrm{F1}=\frac{2tp}{2tp+fp+fn},$ (3)

making it the harmonic mean of precision and recall. However in order to
adjust the measure for the multiclass data used in this paper we use the
micro-averaged values. Microaveraging takes the sum of $tp,fp,fn$ across all
classes, and uses those summed values to calculate the final precisions,
recall, F1 values. As an example micro precision can be defined as
$\bar{tp}_{\mu}=\frac{\sum^{m}_{i}tp_{i}}{\sum^{m}_{i}tp_{i}+\sum^{m}_{i}pp_{i}}$,
where $i$ is the property class and $m$ is the total number of properties. It
should be noted that although the micro average can produced biased results in
imbalenced data, the output put is more easily interpretable as it shows in
simple terms the fraction labels that are correct.

Once the final enhanced dataset has been created the process of analysis can
begin.

###  2.3 Analysing the offshore dataset

The analysis of this paper is performed on each domestic property class. Where
the domestic property classes are conventional homes, low-use homes, airbnb
properties, offshore homes. The purpose of the analysis is to understand the
differences in the behaviour between these property classes and the
implications for the housing market. Three core analyses are performed in this
paper, inferring property price, entropy of property and spatial
autocorrelation. Having described the outline of the method the next section
describes how the total number of UDPs will be calculated.

####  2.3.1 Calculating the total number of unconventional domestic property

The three property types airbnb, low-use that make up UDP are not mutually
exclusive and so cannot simply be added together. Instead the joint
probability will be found under two conditions; The first is that data are
independent; The second that offshore property is always low-use. The first
case is the null hypothesis and will act as the upper bound, whilst the second
case is based on offshore properties being inherently luxury purchases and the
owner having multiple homes. The second case reduces the total number of
classes as the offshore properties become a subset of the low-use properties.
The probability of a UDP in a given LSOA is defined as

$P(U|m)=1-\prod_{i=1}^{m}(1-P(i|m)),$ (4)

That is 1 minus the joint probability of a property not being any UDP type.
Although the number of properties will vary if sampled from the distribution
the variance will be small compared to the difference between the two cases
where UDP types are independent or not. Therefore for simplicity the total
number of UDPs in an LSOA is calculated to be the product of the total number
of homes and the probability of being an UDP in that LSOA. With the number of
UDPs calculated the next section will describe the sampling process applied to
the number of UDPs to infer their mean prices.

####  2.3.2 Inferring the prices of properties in London

In [15] The value of domestic property and low use property was inferred using
a simple graphical model. The same technique will be used in this work to
infer the value of the distinct UDP types as well as for the total number of
UDPs. Due to low the number of samples in certain areas, the LSOA counts will
be aggregated up to MSOA level.

The distribution of the mean price by property type is calculated as shown in

$\left\langle C\right\rangle_{m=i}=\frac{1}{z}\sum_{j}^{z}C_{j}P(C_{j}\mid
m=i),$ (5)

Where $\left\langle C\right\rangle_{m=i}$ is the mean price for property type
$m$, $C_{j}$ is the vector of possible prices for $j$th property and
$P(C_{j}\mid m=i)$ is the probability price distribution for $C_{j}$. The sum
across all values of $j$ is divided by the total number of properties $z$.
Clearly $\left\langle C_{j}\right\rangle_{m=i}$ is simply the mean price of
the MSOA of $j$ as such equation 5 is the Bayesian equivalent mean of the mean
MSOA price weighted by distribution of homes. Both methods can be used and for
large values will produce similar results, however equation 5 is able to
provide an indication of the variance statistical significance and so will be
used here. The equation is implemented as form of ancestral sampling from the
distribution, in order to obtain a representative mean price, the sampling is
repeated 501 times to produce a distribution of the mean, this is similar to
the bootstrap technique developed by [43].

####  2.3.3 Analysing the entropy of domestic property

In information theory Shannon entropy [44] is used to describe the amount of
disorder within a system of a discrete fixed number of states and is measured
in the amount of bits of information required to store it. The higher the
number of bits the more disorder there is in a system and conversely the lower
the number of bits the more order there is in a system and the easier it is to
describe. The equation for Shannon entropy is

$E=-\sum_{i=1}^{i=k}p_{i}\mathrm{log}p_{i},$ (6)

where $k$ is total number of states, and $p_{i}$ is the probability of the
$i$th state.

In the geospatial analysis being performed here the entropy describes how
concentrated the offshore property types are. The discrete states in this case
are the 4835 LSOA in London. The entropy would be minimised if all of a
certain property class would be found in a single LSOA whilst entropy would be
maximised if the properties were spread evenly across all LSOA. As such it can
be said that entropy represents how concentrated the property classes are.

Although understanding the concentration of the property is very valuable it
lacks any spatial information, for this we need to use a measure of spatial
auto-correlation.

####  2.3.4 Analysing the spatial auto-correlation of domestic property

Spatial auto-correlation measures the strength of the correlation between
objects in physical space and a given property. That is, if objects close
together have very similar values but objects far away are very different,
there will be a high auto-correlation value; whilst if objects that a close by
have a very different value there will be a very low value-of auto-
correlation. In this paper we will use Moran’s I [45] as the measure for
spatial auto-correlation. Moran’s I is described as

$I={\frac{k}{W}}{\frac{\sum_{i=1}^{k}\sum_{j=1}^{k}w_{ij}(x_{i}-{\bar{x}})(x_{j}-{\bar{x}})}{\sum_{i=1}^{k}(x_{i}-{\bar{x}})^{2}}},$
(7)

where $k$ is the total number of spatial indexes in this case LSOA, $x$ is the
target characteristic e.g. number of offshore properties, $\bar{x}$ the mean
characteristic value, $W$ is the matrix of weights and $w_{i,j}$ is element
$i$,$j$ of the weights matrix. Similar to the more commonly used, non-spatial,
Pearson correlation, Moran’s I varies between -1 and 1, where 1 shows perfect
positive correlation.

Having described both the data processing and the analysis methods, we will
now introduce the results of both parts of this method.

##  3 Results

The Results section is broken into two parts the first describing the data
processing and the second the analysis of the enhanced OCOD dataset.

###  3.1 Labelling and parsing performance

Table 3 shows the labelling results of the NER task. These results are similar
for both the basic process and using a trained neural network. Overall the
model scored an f1 of 0.96 although this was somewhat unevenly distributed
across the categories. In order to accurately locate and class the addresses
building name, street number, street name and unit id are required (postcode
is already supplied), using just these metrics the mean f1 is 0.95.

Once the labelled addresses have been parsed and expanded the process of
locating the properties and classifying them is performed. The f1 of the
classification process is 0.94 across all classes and 0.96 for domestic
properties with a precision of 0.93 and a recall of 0.99, as shown in table 4.
This means that whilst almost all the domestic properties are being correctly
tagged the process is slightly over-estimating the number of domestic
properties and under estimating the total number of businesses. Counting only
unique title numbers instead of properties reduces the f1 to 0.92 as a large
number of nested domestic properties are discounted

class | precision | recall | fscore | support
---|---|---|---|---
building name | 0.93 | 0.89 | 0.91 | 383.0
city | 1.00 | 0.97 | 0.98 | 947.0
number filter | 1.00 | 1.00 | 1.00 | 23.0
postcode | 1.00 | 1.00 | 1.00 | 768.0
street name | 1.00 | 0.95 | 0.97 | 1029.0
street number | 0.98 | 0.98 | 0.98 | 678.0
unit id | 0.98 | 0.92 | 0.95 | 370.0
unit type | 1.00 | 0.97 | 0.98 | 488.0
Table 3: The NER labelling task is uneven across the entity types but overall provides an f1 score of 0.95-0.96, depending on whether only core labels or all labels are considered. class | precision | recall | fscore | support
---|---|---|---|---
airspace | 1.00 | 0.93 | 0.96 | 14.0
business | 0.97 | 0.78 | 0.87 | 311.0
carpark | 1.00 | 0.96 | 0.98 | 26.0
domestic | 0.93 | 0.99 | 0.96 | 918.0
land | 1.00 | 0.99 | 1.00 | 179.0
unknown | 0.00 | 0.00 | 0.00 | 9.0
Table 4: The performance of the classification task shows that the most
challenging type are domestic and business.

As the labelling and classification f1 scores show good quality results
subsequent analysis of the data can be treated as having a reliable
foundation. The next section will use the enhanced OCOD dataset to provide
insight into UDPs in London. Overall the enhaced ocod dataset has 138 thousand
entries 44% more than the 94 thousand in the original dataset. Table 5
provides the breakdown by property use of the enhanced OCOD dataset using the
type 2 classification.

| class | counts | percentage
---|---|---|---
1 | airspace | 1667 | 1
2 | business | 21396 | 16
3 | carpark | 2438 | 2
4 | domestic | 94576 | 69
5 | land | 16384 | 12
6 | unknown | 1124 | 1
Table 5: Breakdown of property type using type 2 classification

###  3.2 Data analysis

Analysing the subset of the enhanced OCOD dataset that is domestic property in
London and comparing it to Airbnb, low-use and conventional property reveals
certain characteristics of these property types. A summary of the quantitive
analysis can be seen in table 6. Figure 4 shows the total value of the
offshore properties is approximately £56 Billion, this is slightly than the
total value of airbnb property of £42 Billion, however both are less than the
value of the low-use property in London which is £85 Billion. Although the
total amounts are interesting, it is more insightful to compare the mean value
of the property types. There are significant differences in the mean values of
the the property types as shown in Figure 3. Mean value of the Offshore
property is £1.33 million, substantially higher than Airbnb properties which
have a mean value of £1 million, this is followed by Low-use properties at
£900 thousand. Overall the mean value of the UDPs is £1.02 million. However,
all UDPs are much more expensive than the mean conventional property price of
£600 thousand.

Figure 5 shows the distribution of the different property types across the
capital. The map shows that offshore property is the most concentrated with a
large fraction focused in the centre of the city. Airbnb properties are not as
concentrated but appear to have high levels of spatial auto-correlation. In
contrast the distribution of empty homes appears relatively widely dispersed
and with lower levels of spatial auto correlation. The visual interpretation
of Figure 5 is supported by the quantative analysis using entropy and Moran’s
I. Figure 6 shows that the entropy of the offshore property is substantially
higher than that of the other property types. The mean entropy of the
different property types is 9.21 for offshore, 10.8 for airbnb, 11.4 for low-
use and 11.2 for all UDPs. This entropy ranking matches the mean value ranking
of the property types, however this is not the case with the spatial auto-
correlation. Table 6 shows that offshore property has a low spatial auto-
correlation (0.47) compared to the other UDP types (airbnb 0.72, low-use
0.61). It should be noted that figures 4 and 3 have very small variance, there
is little doubt the differences between the categories is statistically
significant.

| type | total value (£B) | counts (K) | mean value (£M) | bits | morans I
---|---|---|---|---|---|---
1 | airbnb | 42.30 | 41 | 1.03 | 10.81 | 0.72
2 | low_use | 85.10 | 93 | 0.91 | 11.39 | 0.61
3 | offshore | 56.10 | 42 | 1.33 | 9.21 | 0.47
4 | homes | 2287.70 | 3553 | 0.64 | 12.19 | 0.33
5 | UDP | 173.80 | 170 | 1.02 | 11.20 | 0.70
Table 6: The complete metrics list for the analysis of the main property types
Figure 3: All three of the property types under analysis are worth
susbtantially more than regular homes in the capital. Airbnb properties are
worth more than low-use homes, highlighting the importance of location for
this property type that exceeds that of general low-use property. However,
offshore property is by far the most expensive of all the property types
Figure 4: Due to the quantity of low use homes, their total value dwarfs that
of the offshore domestic property, even though offshore domestic property is
susbtantially more expensive Figure 5: Looking at the maps clearly shows that
there is a substantial difference in the distribution of the three property
types. Offshore domestic property has some extremely high concentrations where
an entire housing development is owned by an offshore company. Grey areas have
no properties of that type present Figure 6: The entropy of the property types
appears inversely proportional to the value. Indicating that the offshore,
airbnb and low-use properties are not only more concentrated geographically
that normal homes but are more concentrated into high value areas.

####  3.2.1 Nested vs single offshore properties

The notably low spatial auto-correlation value for offshore property compared
to the other UDP types requires further investigation. One of the interesting
aspects of the OCOD dataset is that it contains nested addresses. When the
nested and single addresses are analysed separately a clear separate dynamic
emerges. The single offshore properties show relatively high spatial auto-
correlation with a value of 0.656 and a similarly low entropy as previously.
However, the nested properties have a dramatically lower spatial auto
correlation of 0.112 and a much lower entropy of 7.35 bits. There is also a
substantial difference in the estimated values of these properties with the
individual properties having a higher value of£1.5 Million whilst the nested
properties are closer to the value of conventional housing with a mean value
of £890 thousand. Table 7, provides the full comparison of this analysis,
whilst figure 7, maps the differences between the two types of offshore
property. Looking at the price paid for individual properties provided in the
original OCOD dataset, shows the mean to be £1.8 Million, removing all
properties that are suspiciously low and the mean value increases to £3
Million. However, the prices in the OCOD dataset have a lot of quality issues
and businesses that have been miss-classified will easily skew the total.
Having said some properties within the dataset do have confirmed sales prices
of over £100 million. Overall even though it may be slightly conservative the
sampling method prevents unrealistically high estimates.

| type | total value (£B) | counts (K) | mean value (£M) | bits | morans I
---|---|---|---|---|---|---
1 | individual | 77.60 | 51 | 1.51 | 9.55 | 0.66
2 | nested | 17.30 | 16 | 1.06 | 7.35 | 0.11
Table 7: The complete metrics list comparing nested and single offshore homes
Figure 7: Comparing the nested vs normal property shows very different spatial
patterns. These patterns correspond to large average differences in value.
Grey areas have no properties of that type present

####  3.2.2 Country of Incorporation

There are clear patterns in the London domestic property market with regards
country of incorporation of offshore companies. Whilst there are properties
associated with 133 different countries or territories, 50% of the properties
are related to companies incorporated in the three crown dependencies of
Jersey, Guernsey and the Isle of Man. A further 30% of properties are
associated with one of The British overseas territories. In fact only 4
territories, British Virgin Islands, Jersey, Guernsey and The Isle of Man, are
associated with 78% of all properties. Looking at the relationship with nested
properties, The crown dependencies have an over representation of nested
properties with The crown dependencies and the BVI representing 74% of all
nested titles and 91% of all nested properties. Jersey alone represents 52% of
all nested properties. More detail can be found in supplementary material.

##  4 Discussion

The overall labelling quality was high, an f1 score of 0.96 suggests that the
data is being effectively labelled and provides confidence in the quality of
the final addresses. This also shows that the deduction used in the type two
classifier is a robust approach. On top of this the final classification
values also had strong performance across the categories although separating
businesses and domestic properties was problematic at times. Improving the
performance of the NER process would require a more in depth analysis of why
buildings were not being captured at the same level as the other entities
possibly including large scale visual data and as such is beyond the scope of
this paper. Recall was notably lower for the buildings labels. This was
partially due to the complexity of building names, but the high precision
suggests that there may be some patterns which have not been properly
addressed. A failure to identify evenly patterns from across all building
names highlights a drawback of the weak learning approach and a risk of
inducing systemic bias. It was surprising that the RoBERTa based spaCy model
did not improve performance, this may be because the structure of the
addresses is so simple and regular that the attention abilities of the
transformer could not add any value. The lower recall and precision scores for
Business and Domestic properties was mostly due to these two classes being
difficult to separate. This challenge highlights the importance of the UPRN
system in the UK. It should be noted that a freedom of information request was
sent to both the VOA and the Land Registry requesting they provide the UPRN
data, however, both FOI’s were rejected.

Although undoubtedly the most appropriate choice for the OCOD dataset, the
parser developed for this paper is a niche product designed for a single use
case and is unlikely to offer the robustness or flexibility of already
available general purpose parsers such as Deepparse [22] and libpostal [23].
We would not recommend it as a first choice for parsing data other than the
OCOD dataset and possibly other UK government address data. The code for
creating the enchanced OCOD dataset is publicly available under a GNU GPLv3
license. In addition the supplementary material provides a more in depth
description of the training details. Having the trained pipeline available
means those who wish to use the enhanced OCOD dataset can do so for any past
or future release of the Land Registry’s OCOD dataset.

The results of analysing the enhanced OCOD dataset show that there is a
substantial amount of UDP in London (Property that is either low-use, Airbnb
or offshore owned). There is an estimated £145-174 billion UDP in London. This
is approximately 7.5% of the total domestic value, of this there is
approximately £56 Billion offshore owned property representing 42 thousand
dwellings. We found that there were two distinct types of offshore property
single and nested. The single properties are may be under valued as an
analysis of the prices in the OCOD dataset suggest a mean value of £3 Million
whilst the empirical sample only returns £1.5 Million. However, due to data
quality concerns with the values reported in OCOD the mean reported value
should only be considered as context. In the dataset nested properties, those
properties sharing a title number, are mostly entire housing complexes and
have very different spatial patterns to the individual offshore properties.
Individual offshore properties are very expensive even by the standards of
UDP, in addition they are concentrated on the centre of London with strong
spatial auto correlation. In contrast nested offshore property is somewhat
less concentrated on central London but more highly concentrated in general,
there is also almost no spatial correlation. There was a brief analysis
relating to country of incorporation. Four territories control 78% of all
offshore owned domestic property London. These territories are the three UK
Crown Dependencies (Jersey, Guernsey, The Isle of Man) and The British Virgin
Islands, which is a British Overseas Territory. Although we do not suggest
that any specific property is being used to launder money, throughout the
project certain properties did show some unusual characteristics; However a
deeper inspection of these properties is beyond the scope of this paper.

##  5 Conclusion

This paper proposes a new method of identifying and classifying offshore owned
domestic property in England and Wales. Using the dataset created by this
method we have shown that Offshore owned domestic property in London is not a
homogeneous group, but contains at least two distinct types, characterised by
measurable quantities and appearing to relate to purpose and country of
incorporation. We have also shown the distribution and value of three
different types of UDP and that all three property types are more concentrated
and more expensive than conventional domestic property. Whilst this paper does
not draw any causal relations or make any policy proposals it provides for the
first time a clear map to understanding the behaviours and value of Offshore
owned domestic property and Low-use property in London. This data can be used
by sociologists, economist and policy makers to ensure that attempts to reduce
money laundering and high property prices are based on detailed data that
reflect the real situation. In addition to the findings of the analysis the
paper has created a high quality open-source dataset that allows further
research as well as a code pipeline to create new datasets as required. It has
been said that fighting money laundering requires openness and transparency
[5], the OCOD dataset was a step in that direction, we hope that the Enhanced
OCOD dataset is the next.

###  5.1 Future work

The enhanced OCOD dataset can be used for a wide variety of projects. Future
work can expand the offshore analysis to the entire of England and Wales.
Building on the work of [11], and analysis of the nested offshore properties
can be performed to explore whether they are vehicles for gentrification. Some
of the properties appeared to have unusual characteristics, work of [7] could
be used to identify if these properties may be being used for money
laundering.

##  6 acknowledgements

We would thank humanloop.com for giving us access to the ‘Programmatic’ beta
and providing technical support during the NER labelling phase of the project.

##  7 funding

The authors would like to thank Trust for London (grant number
MAIN-S2-06.10.2020-8792(6930) ) for funding the research. We would also like
to thank Kingston University for providing additional research funding.

## References

  * [1] TIUK.  Stats reveal extent of suspect wealth in UK property and Britain’s role as global money laundering hub, 2022.
  * [2] Ben Cowdock and Steve Goodrich.  The Cost of Secrecy.  Technical report, Transparency International, London, 2018.
  * [3] HM Treasury.  National risk assessment of money laundering and terrorist financing 2020\.  Technical report, UK Government, London, 2020.
  * [4] Matteo de Simone, Lloyd Barthropp, and Nicl Maxwell.  Corruption on your doorstep: How corrupt capital is used to buy property in the UK.  Technical report, Transparency International, 2015.
  * [5] Austin Mitchell, Prem Sikka, and Hugh Willmott.  The Accountants’ Laundromat.  Association for Accountancy & Business Affairs, Basildon, 1998.
  * [6] Milind Tiwari, Adrian Gepp, and Kuldeep Kumar.  A review of money laundering literature: the state of research in key areas.  Pacific Accounting Review, 32(2):271–303, January 2020.  Publisher: Emerald Publishing Limited.
  * [7] Brigitte Unger and Joras Ferwerda.  Money Laundering in the Real Estate Sector.  Edward Elgar Publishing, 2011.  Publication Title: Books.
  * [8] Private Eye.  Selling England (and Wales) by the pound, 2015.
  * [9] Land Registry.  Overseas companies that own property in England and Wales - Use land and property data, 2022.
  * [10] Rex McKenzie and Rowland Atkinson.  Anchoring capital in place: The grounded impact of international wealth chains on housing markets in London.  Urban Studies, 57(1):21–38, January 2020.  Publisher: SAGE Publications Ltd.
  * [11] Filipa Sá.  The Effect of Foreign Investors on Local Housing Markets: Evidence from the UK.  SSRN Scholarly Paper 2877263, Social Science Research Network, Rochester, NY, November 2016.
  * [12] Zahratu Shabrina, Elsa Arcaute, and Michael Batty.  Airbnb and its potential impact on the London housing market.  Urban Studies, 59(1):197–221, January 2022.  Publisher: SAGE Publications Ltd.
  * [13] Helen X. H. Bao and Saul Shah.  The Impact of Home Sharing on Residential Real Estate Markets.  Journal of Risk and Financial Management, 13(8):161, August 2020\.  Number: 8 Publisher: Multidisciplinary Digital Publishing Institute.
  * [14] Miquel-Àngel Garcia-López, Jordi Jofre-Monseny, Rodrigo Martínez-Mazza, and Mariona Segú.  Do short-term rental platforms affect housing markets? Evidence from Airbnb in Barcelona.  Journal of Urban Economics, 119:103278, September 2020.
  * [15] Jonathan Bourne.  Empty homes: mapping the extent and value of low-use domestic property in England and Wales.  Palgrave Communications, 5(1):1–14, February 2019.  Number: 1 Publisher: Palgrave.
  * [16] Youngre Noh, Galen Newman, and Ryun Jung Lee.  Urban decline and residential preference: The effect of vacant lots on housing premiums.  Environment and Planning B: Urban Analytics and City Science, 48(6):1667–1683, July 2021.  Publisher: SAGE Publications Ltd STM.
  * [17] Hiroki Baba and Kimihiro Hino.  Factors and tendencies of housing abandonment: An analysis of a survey of vacant houses in Kawaguchi City, Saitama.  JAPAN ARCHITECTURAL REVIEW, 2(3):367–375, 2019.  _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2475-8876.12088.
  * [18] Land Registry.  How to access HM Land Registry Price Paid Data, 2022.
  * [19] OS.  OS Open UPRN, November 2021.
  * [20] Bin Chi, Adam Dennett, Thomas Oléron-Evans, and Robin Morphet.  A new attribute-linked residential property price dataset for England and Wales, 2011 to 2019.  UCL Open: Environment Preprint, May 2021.  Publisher: UCL Press.
  * [21] Sam Comber and Daniel Arribas-Bel.  Machine learning innovations in address matching: A practical comparison of word2vec and CRFs.  Transactions in GIS, 23(2):334–348, 2019.  _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12522.
  * [22] Marouane Yassine, David Beauchemin, François Laviolette, and Luc Lamontagne.  Leveraging Subword Embeddings for Multinational Address Parsing.  In 2020 6th IEEE Congress on Information Science and Technology (CiSt), pages 353–360, June 2020.  ISSN: 2327-1884.
  * [23] Al Barratine.  libpostal: international street address NLP, 2017.  original-date: 2015-03-03T00:21:31Z.
  * [24] Selman Delil, Birol Kuyumcu, Cüneyt Aksakallı, and İsa Semih Akçıra.  Parsing Address Texts with Deep Learning Method.  In 2020 28th Signal Processing and Communications Applications Conference (SIU), pages 1–4, October 2020.  ISSN: 2165-0608.
  * [25] OSM.  OpenStreetMap, 2004.
  * [26] Helen Craig, Dragomir Yankov, Renzhong Wang, Pavel Berkhin, and Wei Wu.  Scaling Address Parsing Sequence Models through Active Learning.  In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’19, pages 424–427, New York, NY, USA, November 2019. Association for Computing Machinery.
  * [27] Victor S. Sheng and Jing Zhang.  Machine Learning with Crowdsourcing: A Brief Summary of the Past Research and Future Directions.  Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):9837–9843, July 2019.  Number: 01.
  * [28] Jing Zhang, Xindong Wu, and Victor S. Sheng.  Learning from crowdsourced labeled data: a survey.  Artificial Intelligence Review, 46(4):543–576, December 2016.
  * [29] Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, and Christopher Ré.  Data Programming: Creating Large Training Sets, Quickly.  arXiv:1605.07723 [cs, stat], January 2017.  arXiv: 1605.07723.
  * [30] Pierre Lison, Aliaksandr Hubin, Jeremy Barnes, and Samia Touileb.  Named Entity Recognition without Labelled Data: A Weak Supervision Approach.  arXiv:2004.14723 [cs, stat], April 2020.  arXiv: 2004.14723.
  * [31] Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré.  Training Complex Models with Multi-Task Weak Supervision.  arXiv:1810.02840 [cs, stat], December 2018.  arXiv: 1810.02840.
  * [32] Jason A. Fries, Ethan Steinberg, Saelig Khattar, Scott L. Fleming, Jose Posada, Alison Callahan, and Nigam H. Shah.  Ontology-driven weak supervision for clinical entity classification in electronic health records.  Nature Communications, 12(1):1–11, April 2021.  Number: 1 Publisher: Nature Publishing Group.
  * [33] Curtis G. Northcutt, Anish Athalye, and Jonas Mueller.  Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.  arXiv:2103.14749 [cs, stat], November 2021.  arXiv: 2103.14749.
  * [34] ONS.  Census geography, 2022.
  * [35] VOA.  VOA rating list downloads, 2017.
  * [36] ONS.  ONSPD, 2022.
  * [37] Murray Cox, John Morris, and Taylor Higgins.  Inside Airbnb, 2022.
  * [38] Hadley Wickham.  Tidy Data.  Journal of Statistical Software, 59:1–23, September 2014.
  * [39] Humanloop.  Humanloop: Programmatic, 2022.
  * [40] Matthew Honnibal and Ines Montani.  spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing, 2017.
  * [41] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov.  RoBERTa: A Robustly Optimized BERT Pretraining Approach.  arXiv:1907.11692 [cs], July 2019.  arXiv: 1907.11692.
  * [42] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin.  Attention Is All You Need.  arXiv:1706.03762 [cs], December 2017.  arXiv: 1706.03762.
  * [43] Bradley Efron and Robert Tibshirani.  An introduction to the bootstrap.  Chapman & Hall/CRC, Philadelphia, PA, 1993.
  * [44] C. E. Shannon.  A mathematical theory of communication.  The Bell System Technical Journal, 27(3):379–423, July 1948.  Conference Name: The Bell System Technical Journal.
  * [45] P. A. P. Moran.  Notes on Continuous Stochastic Phenomena.  Biometrika, 37(1/2):17–23, 1950.  Publisher: [Oxford University Press, Biometrika Trust].