Quarterly house price data based on a sub-sample of the Regulated Mortgage Survey.

The UK House Price Index (UK HPI) is an official statistic that captures changes in the value of residential properties in the United Kingdom. The UK HPI is calculated by the Office for National Statistics and Land & Property Services Northern Ireland. Data for the UK House Price Index is provided by HM Land Registry, Registers of Scotland, Land & Property Services Northern Ireland and the Valuation Office Agency. Geographic coverage England, Scotland, Wales and Northern Ireland License statement UK HPI data is published under Open Government Licence. When using or publishing data from the UK HPI reports, background tables in the statistical datatset: UK House Price Index: data downloads or search tool, you will need to add the following attribution statement: Contains HM Land Registry data © Crown copyright and database right [year of supply or date of publication]. This data is licensed under the Open Government Licence v3.0. When you publish the data, be sure to include information about the nature of the data and any relevant dates for the period of time covered. Neither HM Land Registry nor any third party shall be liable for any loss or damage, direct, indirect or consequential, arising from: any inaccuracy or incompleteness of the data in the UK HPI any decision made or action taken in reliance upon the data Neither shall HM Land Registry or any third party be liable for loss of business resources, lost profits or any punitive indirect, consequential, special or similar damages whatsoever, whether in contract or tort or otherwise, even if advised of the possibility of such damages being incurred.

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Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

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Abstract copyright UK Data Service and data collection copyright owner.

The aim of this survey was to collect data on the housing situations of Asian and British households in the areas of South Manchester and North Cheshire. Data were collected on the expenditure and location patterns of the households as well as on their searching behaviour and means of finance.
Main Topics:

Housing history, reasons for moving, search process, reasons for choosing a house. Location and quality of home, details of move, future plans. Present and previous accommodation (age of tenure, number of rooms, amount of loan, monthly repayments, buying and selling prices).
Background Variables
Age, sex, marital status, type of household, number of children (number born at present address), number of others living in accommodation. Social class and socio-economic group of head of household. Income of head of household and spouse both at time of move and at present. Details of parents' accommodation. Location of respondent's workplace.
Distribution of ethnic groups, tenure, population density in households and household amenities in local enumeration district.

Snapshot img

Europe Online Home Rental Services Market Size 2025-2029

The Europe online home rental services market size is forecast to increase by USD 6.35 billion at a CAGR of 11.8% between 2024 and 2029.

The Online Home Rental Services Market is experiencing significant growth, driven by the increasing internet penetration and digitalization of services. With more consumers turning to online platforms for convenience and ease, the market is poised for continued expansion. However, navigating diverse and fragmented regulatory landscapes poses a challenge. Regulatory hurdles impact adoption in certain regions, requiring companies to adapt and comply with local regulations. Additionally, the increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies in online home rental services offers opportunities for enhanced customer experiences and improved operational efficiency. The integration of Internet of Things (IoT) technology with building automation software is a key trend driving market expansion.
Companies seeking to capitalize on market opportunities must stay abreast of regulatory changes and invest in advanced technologies to differentiate themselves and meet evolving consumer demands. Effective strategic planning and agile business models are essential for success in this dynamic market.

What will be the size of the Europe Online Home Rental Services Market during the forecast period?

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In the online home rental services market, personalized recommendations based on customer personas and user behavior play a significant role in user engagement and conversion rates. Rental agreements are streamlined through pricing algorithms, ensuring fairness and transparency. Mobile optimization and voice search cater to on-the-go customers, driving growth. Support services have been fostered through connected devices, machine learning, and artificial intelligence, reducing customer acquisition costs. Cleaning fees and security deposits are managed efficiently, while fraud prevention measures protect both hosts and guests. Seasonal trends and green initiatives influence demand patterns, requiring adaptability from service providers. Smart home technology and property automation enhance the user experience, increasing lifetime value. Renewable energy solutions and building information modeling are essential trends in the market, as businesses and organizations strive for sustainability and cost savings.
Host ratings, guest reviews, and payment security ensure trust and transparency, while remote access and data privacy maintain user confidence. Brand awareness is boosted through content marketing, influencer marketing, and insurance coverage. Property verification, identity verification, and automated processes streamline operations and improve security protocols. The Internet of Things and energy efficiency are key trends, with voice search and virtual assistants simplifying user interactions. Machine learning and artificial intelligence enable personalized services and fraud prevention, while user engagement remains a top priority. Growth in demand for tiny home structures is the primary trend in the online home rental services market. The market continues to evolve, with new technologies and trends shaping the future of online home rental services.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Flats
  Semi-detached houses
  Detached houses


Rental Category

  Monthly
  Weekly
  Daily
  Yearly


Service

  Economy
  Mid-range
  Premium


End-user

  Resident
  Tourist


Geography

  Europe

    France
    Germany
    Italy
    UK

By Type Insights

The flats segment is estimated to witness significant growth during the forecast period. In the dynamic world of online home rentals, flats, or multi-unit dwellings, hold a significant position, catering to the diverse housing needs of individuals and families in Europe. Urbanization trends have fueled the demand for residential options in densely populated areas, making flats the preferred choice in many cities. Online platforms have revolutionized the rental market, offering a seamless experience for both landlords and tenants. Property photos showcase available units, while email marketing campaigns attract potential renters. Property data ensures accurate and up-to-date listings, enabling users to make informed decisions. The market caters to various segments, including luxury rentals and unique properties, which appeal to discerning tenants.

Mobile apps, search engine optimization, and social media marketing expand visibility, while property amenities and virtual tours provide detailed information. Competitor analy

This Web service provides the BGS Thermal Properties (1 km hex grid) dataset as a Web Map Service (WMS). This dataset shows thermal properties relating to bedrock beneath our feet. The information can be used to assess the potential for closed and open loop ground source heat pumps across, or deeper geothermal assessments, across the United Kingdom. The attribution and spatial data underpinning the model are that which is described and shown by Rollin (1987) and Gale (2004, 2005).

This web map service (WMS) depicts estimates of mean values of soil bacteria, invertebrates, carbon, nutrients and pH within selected habitats and parent material characteristics across GB . Estimates were made using CS data using a mixed model approach. The estimated means of habitat/parent material combinations using 2007 data are modelled on dominant habitat and parent material characteristics derived from the Land Cover Map 2007 and Parent Material Model 2009, respectively. Bacteria data is representative of 0 - 15 cm soil depth and includes bacterial community structure as assessed by ordination scores. Invertebrate data is representative of 0 - 8 cm soil depth and includes Total catch, Mite:Springtail ratio, Number of broad taxa and Shannon diversity. Gravimetric moisture content (%) data is representative of 0 - 15 cm soil depth Carbon data is representative of 0-15 cm soil depth and includes Loss-on-ignition (%), Carbon concentration (g kg-1) and Carbon density (t ha-1). Loss-on-ignition was determined by combustion of 10g dry soil at 375 deg C for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55, and carbon density was estimated by combining carbon concentration with bulk density estimates. Nutrient data is representative of 0 - 15 cm soil depth and includes total nitrogen (N) concentration (%), C:N ratio and Olsen-Phosphorus (mg/kg). pH and bulk density (g cm-3) data is representative of 0 - 15 cm soil depth. Topsoil pH was measured using 10g of field moist soil with 25ml de-ionised water giving a ratio of soil to water of 1:2.5 by weight; bulk density was estimated by making detailed weight measurements throughout the soil processing procedure. Areas, such as urban and littoral rock, are not sampled by CS and therefore have no associated data. Also, in some circumstances sample sizes for particular habitat/parent material combinations were insufficient to estimate mean values.