This data collection consists of 18 interview transcripts meant to explore the rationales and methods by which investors in Hong Kong buy properties in the UK. The life and impact of the residential choices of the 'super rich' has been a major strand in research by the research team. This work advanced the proposition that the upper-tier of income groups living in cities tend to exploit particular forms of service provision (such as education, cultural life and personal services), are largely distanced from the mundane flow of social life in urban areas and tend to be withdrawn from the civic life of cities more generally. Some of this work is underpinned by the literature on, for example, gated communities, but it has surprisingly been under-used as the guiding framework for close empirical work in affluent neighbourhoods, perhaps largely as a result of the perceived difficulty of working with such individuals. This project will allow us to generate insights into how super-rich neighbourhoods operate, how people come to live there and the social and economic tensions and trade-offs that exist as such processes are allowed to run. As many people question the role and value of wealth and identify inequality as a growing social problem this research will feed into public conversations and policymaker concerns about how socially vital cities can be maintained when capital investment may undermine such objectives on one level (the creation of neighbourhoods that are both exclusive and often 'abandoned' for large parts of the year), while potentially fulfilling broader ambitions at others (over tax receipts for example).Social research has tended not to focus on the super-rich, largely because they are hard to locate, and even harder to collaborate with in research. In this project we seek to address these concerns by focusing extensive research effort on the question of where and how the super-rich live and invest in the property markets of the cities of Hong Kong and London. We see these cities as exemplary in assisting in the construction of further insights and knowledge in how the super-rich seek residential investment opportunities, how they live there when they are 'at home' in such residences and how these patterns of investment shape the social, political and economic life of these cities more broadly. Given that the super-rich make such decisions on the basis of tax incentives and the attraction of major cultural infrastructure (such as galleries and theatre) we have proposed a program of research capable of offering an inside account of the practices that go to make-up these investment patterns including processes of searching for suitable property, its financing, the kinds of property deemed to be suitable and an analysis of how estate agents and city authorities seek to capitalise and retain the potentially highly mobile investment by the super-rich. In economic terms the life and functioning of rich neighbourhood spaces appears intuitively important. For example, attractive and safe spaces for captains of industry, senior figures in political and non-government organizations are often regarded as major markers of urban vitality and the foundation of social networks that may make-up the broader glue of civic and political society. Yet we know very little about how such neighbourhoods operate, who they attract and how they are linked to other cities and their neighbourhoods globally. Our aim in this research is to grapple with what might be described as the 'problem' of these super-rich neighbourhoods - sometime called the 'alpha territory' - and undertake research that will help us to understand more about the advantages and disadvantages of these kinds of property investment. The research was carried out using semi-structured interviews and participant observation at property fairs and development sites in Hong Kong and different cities in the UK. Moreover, semi-structured interviews were conducted to explore the rationales and methods by which investors in Hong Kong buy properties in the UK. Participants were recruited using searches for relevant key actors as well as accessing personal and professional networks that enabled snowballing techniques to elicit further contacts. Interviews were conducted with individual investors, local government officials, planning officers, inward investment agencies, city government officials and estate agents. Interviews were conducted in both English and Cantonese.

Introduction

Welcome to the UK English Call Center Speech Dataset for the Real Estate domain designed to enhance the development of call center speech recognition models specifically for the Real Estate industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data:

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Real Estate domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Real Estate domain call center conversational AI and ASR models for the UK English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of UK English call center speech recognition models.

Usage and

Abstract copyright UK Data Service and data collection copyright owner.

Further information about the EHS 2019-2020 can be found in the GOV.UK English Housing Survey 2019 to 2020: headline report.

The EHS Housing survey consists of two components.

Interview survey on the participating household - An interview is first conducted with the householder. The interview topics include: household characteristics, satisfaction with the home and the area, disability and adaptations to the home, ownership and rental details and income details. All interviewees are guaranteed confidentiality and all data is anonymised.

Physical survey on the housing Stock - Where interviews were achieved (the ‘full household sample’), each year all rented properties and a sub-sample of owner occupied properties are regarded as eligible for the physical survey and the respondent’s consent is sought. A proportion of vacant properties were also sub-sampled. For all physical survey cases, a visual inspection of both the interior and exterior of the dwelling is carried out by a qualified surveyor to assess the condition and energy efficiency of the dwelling. Topics covered include whether the dwelling meets the Decent Homes Standard; cost to make the dwelling decent; existence of damp and Category 1 Hazards as measured by the Housing Health and Safety Rating System (HHSRS); Energy Efficiency Rating.

This dataset contains data from the interview survey only. The data from the physical survey are available in a separate deposit (the Housing Stock Dataset).

Abstract copyright UK Data Service and data collection copyright owner.

The English Housing Survey (EHS) is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England. The EHS brings together two previous survey series into a single fieldwork operation: the English House Condition Survey (EHCS) (available from the UK Data Archive under GN 33158) and the Survey of English Housing (SEH) (available under GN 33277). The EHS covers all housing tenures. The information obtained through the survey provides an accurate picture of people living in the dwelling, and their views on housing and their neighbourhoods. The survey is also used to inform the development and monitoring of the Ministry's housing policies. Results from the survey are also used by a wide range of other users including other government departments, local authorities, housing associations, landlords, academics, construction industry professionals, consultants, and the general public.

The EHS has a complex multi-stage methodology consisting of two main elements; an initial interview survey of around 12,000 households and a follow-up physical inspection. Some further elements are also periodically included in or derived from the EHS: for 2008 and 2009, a desk-based market valuation was conducted of a sub-sample of 8,000 dwellings (including vacant ones), but this was not carried out from 2010 onwards. A periodic follow-up survey of private landlords and agents (the Private Landlords Survey (PLS)) is conducted using information from the EHS interview survey. Fuel Poverty datasets are also available from 2003, created by the Department for Energy and Climate Change (DECC).

The EHS interview survey sample formed part of the Integrated Household Survey (IHS) (available from the Archive under GN 33420) from April 2008 to April 2011. During this period the core questions from the IHS formed part of the EHS questionnaire.

End User Licence and Special Licence Versions:
From 2014 data onwards, the End User Licence (EUL) versions of the EHS will only include derived variables. In addition the number of variables on the new EUL datasets has been reduced and disclosure control increased on certain remaining variables. New Special Licence versions of the EHS will be deposited later in the year, which will be of a similar nature to previous EHS EUL datasets and will include derived and raw datasets.

Further information about the EHS and the latest news, reports and tables can be found on the GOV.UK English Housing Survey web pages.

The English Housing Survey, 2017: Housing Stock Data is available for all cases where a physical survey has been completed. For occupied cases the data comprises information from the household interview and from the physical survey. For vacant properties only, data from the physical survey are provided. The study only includes derived variables.

The data are made available for a two-year rolling sample i.e. approximately 12,000 cases together with the appropriate two-year weights. For example, the EHS Housing Stock data presented here are for 2017, but cover the period April 2016 to March 2018. This means that if you use more than one housing stock dataset, you must use either odd or even years. For example, you need to use the Housing Stock Dataset for '2012' and '2014' or '2013' and '2015', but not the dataset for '2014' and '2013' as you would double-count the cases surveyed between April 2013 and March 2014. The Housing Stock dataset should be used for any analysis requiring information relating to the physical characteristics and energy efficiency of the housing stock. Derived datasets provide key analytical variables compiled post-fieldwork including energy efficiency ratings, decent home indicators and equivalised income.

Users who only require data from the household interview should use the English Housing Survey, 2017-2018: Household Data (held under SN 8495).

Users should note that the dictionary of derived variables covering the current year will be made available at a later date, alongside the 2017-18 Technical Report.

The EHS Housing Stock survey consists of two components.
Interview Survey
An interview is first conducted with the householder. The interview topics include: household characteristics, satisfaction with the home and the area, disability and adaptations to the home, ownership and rental details and income details. All interviewees are guaranteed confidentiality and all data are anonymised.

Physical Survey
Where interviews were achieved (the 'full household sample'), each year all rented properties and a sub-sample of owner occupied properties...