Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

This report contains details of Environment Agency Corporate properties & Residential properties listing the address, postcode, current use and occupation status. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.

Median monthly rental prices for the private rental market in England by bedroom category, region and administrative area, calculated using data from the Valuation Office Agency and Office for National Statistics.

PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/dcbad548-ba75-4f32-bf22-306f9059343e These documents are designed to provide a simple, easy to refer to analysis of the numbers of people, property and extent of land within areas at risk of flooding taken from the risk of flooding from rivers and sea (RoFRS) products. We use them in Environment Agency publications and reports; and to answer queries.

We use the Risk of Flooding from Rivers and Sea products and the National Receptor Dataset (NRD) 2023* to provide a breakdown of numbers and areas of land at risk of flooding within England, English Local Authorities, English MP Constituencies, Lead Local Flood Authorities and Environment Agency Partnership and Strategic Overview areas.

There are 5 spreadsheets available which provide the following summary information: • Number of properties in areas at risk from flooding from rivers and sea • Number of people in areas at risk from flooding from rivers and sea • Number of deprived properties in areas at risk of flooding from rivers and sea • Change in number of properties in areas at risk of flooding from rivers and sea (since previous update) • Change in area of land at risk of flooding from rivers and sea (since previous update)

Further explanations about the information presented in the spreadsheets such as flood likelihood category, property types and how numbers are calculated are presented in the first tab of each spreadsheet.

*NRD2023 was developed by the Environment Agency, however it is based on Ordnance Survey data (OS Address Base Premium) and we do not have permission to release as Open Data.

This report contains details of Environment Agency corporate property - including address, postcode, grid reference, current use and status. April 2016

This report produced for the Document Management Team contains details of Environment Agency corporate property - including address, postcode, grid reference, current use and status. March 2016 Attribution statement: © Environment Agency copyright and/or database right 2019. All rights reserved.

Abstract copyright UK Data Service and data collection copyright owner. A quarterly survey of house sales in Bedfordshire to monitor trends. Data are collected on the property (location, price, age, type, size); on the vendor (destination); and on the purchaser (age, origin, workplace and reason for move. Questionnaires completed by participating estate agents as sales occur and collected quarterly by Planning Department staff

Introduction

This UK English Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native UK English speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for English real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/b5aaa28d-6eb9-460e-8d6f-43caa71fbe0e This dataset is not suitable for identifying whether an individual property will flood. This bundle includes the full set of datasets from our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is a group of datasets previously available as the uFMfSW Complex Package. Further information on using these datasets can be found at the Resource Locator link below. Information Warnings: Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right

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.

This report produced for Capgemini contains details of Environment Agency corporate property listing the CIS rating - includes address, postcode, grid reference, current use and status. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.

This is the mean (average) gross monthly rent in pounds for properties with one bedroom on the private rental market for the area, over a 12 month period. These are self-contained properties including houses, bungalows, flats and maisonettes. These statistics taken from the Valuation Office Agency (VOA) administrative database are simple price averages rounded to the nearest £1. The sample used to produce these statistics is not statistical and may not be consistent over time; as such, these data should not be compared across time periods or between areas. Housing Benefit claimants are not included in the sample.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/b5aaa28d-6eb9-460e-8d6f-43caa71fbe0e

This dataset is not suitable for identifying whether an individual property will flood. GIS layer showing the maximum speed of flood flow from surface water that could result from a flood with a 3.3% chance of happening in any given year. The speed is grouped into 5 bands. NB: the maximum speed may not happen at the same time as the maximum depth. This dataset is one output of our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is one of a group of datasets previously available as the uFMfSW Complex Package. Further information on using these datasets can be found at the Resource Locator link below. Information Warnings: Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/b5aaa28d-6eb9-460e-8d6f-43caa71fbe0e

This dataset is not suitable for identifying whether an individual property will flood. GIS layer showing the extent of flooding from surface water that could result from a flood with a 1% chance of happening in any given year. The flood depth is grouped into 6 bands. This dataset is one output of our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is one of a group of datasets previously available as the uFMfSW Complex Package. Further information on using these datasets can be found at the Resource Locator link below. Information Warnings: Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

This dataset provides Census 2021 estimates that classify households in England and Wales by dwellings that are HMOs by accommodation type. The estimates are as at Census Day, 21 March 2021.

Improvements to the Census address frame allowed us to accurately list multiple household spaces within the same building. This means the data are more often counted as distinct households within separate dwellings reflecting living arrangements. Read more about this quality notice.

We have made changes to housing definitions since the 2011 Census. Take care if you compare Census 2021 results for this topic with those from the 2011 Census. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Coverage

Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Households of multiple occupancy (HMO)

A dwelling where unrelated tenants rent their home from a private landlord is a HMO, if both of the following apply:

• at least three unrelated individuals live there, forming more than one household
• toilet, bathroom or kitchen facilities are shared with other tenants

A small HMO is shared by 3 or 4 unrelated tenants. A large HMO is shared by 5 or more unrelated tenants.

Accommodation type

The type of building or structure used or available by an individual or household.

This could be:

• the whole house or bungalow
• a flat, maisonette or apartment
• a temporary or mobile structure, such as a caravan

More information about accommodation types

Whole house or bungalow:

This property type is not divided into flats or other living accommodation. There are three types of whole houses or bungalows.

Detached:

None of the living accommodation is attached to another property but can be attached to a garage.

Semi-detached:

The living accommodation is joined to another house or bungalow by a common wall that they share.

Terraced:

A mid-terraced house is located between two other houses and shares two common walls. An end-of-terrace house is part of a terraced development but only shares one common wall.

Flats (Apartments) and maisonettes:

An apartment is another word for a flat. A maisonette is a 2-storey flat.

PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/b5aaa28d-6eb9-460e-8d6f-43caa71fbe0e

This dataset is not suitable for identifying whether an individual property will flood. GIS layer showing information about the modelling used at that location. Including whether local outputs were used to replace the national outputs and other parameters, such as the model software used. This dataset is one output of our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is one of a group of datasets previously available as the uFMfSW Complex Package. Further information on using these datasets can be found at the Resource Locator link below. Information Warnings: Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence.

Corporate Property list created for the National Accommodation Team.

Size, condition and costs of school estate. Source agency: Scottish Government Designation: National Statistics Language: English Alternative title: School Estate Statistics, Scotland

This report contains details of Environment Agency corporate property - including address, postcode, grid reference, current use and status. April 2016 Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.