The Housing Landlord-Tenant Case Tracking dataset includes tracking information, complaints and individual case dispositions. The data is updated monthly.

14% of White British households rented their home privately in the 2 years from April 2021 to May 2023 – the lowest percentage out of all ethnic groups.

Permits paid by and issued to single family residential property owners who do not have homestead exemption.

The dataset contains monthly Landlord/Tenant caseload information by court from January 2023- Present. Landlord/Tenant cases include: 1) Eviction- All suits for eviction (recovery of possession of premises) brought to recover possession of real property under Chapter 24 of the Texas Property Code, often by a landlord against a tenant. A claim for rent may be joined with an eviction case if the amount of rent due and unpaid is not more than $20,000, excluding statutory interest and court costs but including attorney fees, if any. Eviction cases filed on or after September 1, 2023, are governed by Rules 500-507 and 510 for Part V of the Rules of Civil Procedure. 2) Repair and Remedy- A case by a residential tenant under Chapter 92, Subchapter B, of the Texas Property Code to enforce the landlord’s duty to repair or remedy a condition materially affecting the physical health or safety of an ordinary tenant. Repair and remedy cases filed on or after September 1, 2013, are governed by Rules 500-507 and 509 of Part V of the Rules of Civil Procedure. Because of the submission deadlines for reports, the most recent monthly data will be two months behind.

The largest owner of apartments in the United States was Greystar, an international developer and manager headquartered in Charleston, SC. In 2025, Greystar owned nearly ******* units. MAA, a Tennessee-based real estate investment trust, ranked second, with ******* apartments owned. Real estate investment trusts The majority of the largest owners of apartments in the U.S. are real estate investment trusts (REITs), which are companies that own (and usually operate) income-producing real estate. REITs were created in 1960, when the Cigar Excise Tax Extension permitted investment in large-scale diversified real estate portfolios through the purchase and sale of liquid securities. This effectively aligned investment in real estate with other asset classes. In 2023, there were approximately 200 REITs in the United States with a market capitalization of *** trillion U.S. dollars. Apartments in the United States The rental return for apartments in the U.S. has been steadily climbing in recent times, with the national monthly median rent for an unfurnished apartment steadily increasing since 2012. Over this period, apartment vacancy rates have been decreasing across the country, suggesting that demand outweighs supply. Accordingly, large-scale investment in apartments by REITs is likely to continue into the foreseeable future.

As per Cognitive Market Research's latest published report,The Europe Landlord Insurance market size will be $27,770.62 Million by 2028.The Europe Landlord Insurance Industry's Compound Annual Growth Rate will be 7.94% from 2023 to 2030. What is Driving Landlord Insurance Industry Growth?

Rising demand of rental properties

It is said that the best investment is a land investment. Population across the globe follows these proverbs and invest their saving in buying homes. The housing process in European countries were observed at its peak which were derived by the large investors. The institutional investors including private equity and pension funds has raise the houses prices in the European countries. The volume of purchases in Europe hit €64bn (£53bn) in 2020, with about €150bn value of housing stock conservatively estimated to be in the hands of such large investors. According to Preqin private database of investors, Berlin, with €40bn worth of housing assets in institutional portfolios is at top followed by London, Amsterdam, Paris and Vienna.

The data from Berlin’s Free University states that the Europe’s housing has become increasingly attractive asset class for investors owing to near-zero interest rates and cheering regulatory outlines. The data from European central bank shows that the real estate funds in the Eurozone reached €1tn in 2021 in which residential assets are consider as progressively central part. The institutional investors’ residential transactions between 2012 and 2021 was increased in Germany, Denmark followed by Netherlands.

Significant occupancy of residential and commercial properties by institutional investors led to the undersupply of housing across the continent and results in the increasing rental rates. Owing to the chronic undersupply of housing in several European countries, the population of the tenants increases which simultaneously increases the demand of rental properties in Europe. Moreover, the capability of population to purchase house is also decreasing with the increasing annual house prices. The data shows a surge in rents by 16.0 % and house prices by 38.7 % from 2010 to third quarter of 2021 in Europe. The rent and houses price in Europe has increased by 1.2 % and 9.2 % respectively from third quarter of 2021 to third quarter of 2020.

Landlord insurance is applicable to rental properties only. Hence, with the increasing demand of rental properties in Europe is driving the growth of landlord insurance market.

Increase in natural disasters is propelling market growth

Restraint of the Europe Landlord Insurance Market

Inadequate information related to landlord insurance policies.(Access Detailed Analysis in the Full Report Version)

Opportunities of the Europe Landlord Insurance Market

Introduction of new technologies in insurance industry.(Access Detailed Analysis in the Full Report Version)

What is Landlord Insurance?

Landlord Insurance is a sort of homeowner's insurance that protects homeowners against financial losses associated with rental properties. This insurance includes coverage for fire and other dangers, as well as theft and intentional damage.

Several European nations are quickly implementing landlord insurance for their buildings. Property and liability protection are two forms of coverage that are commonly included in insurance policies. Both insurance policies are designed to protect both the landlord and the renters from financial losses.

Damage to property, income replacement, liability insurance, and add-on coverage are all covered by landlord insurance. It assists clients in protecting themselves from financial losses caused by natural catastrophes, injuries, accidents, and other liability concerns.

It also provides payment for lost rent, repairs, and property replacement that are covered by landlord insurance.

Landlord liability insurance, landlord buildings insurance, landlord contents insurance, loss of rent insurance, tenant default insurance, accidental damage insurance, alternative accommodation insurance, unoccupied property insurance, and legal expenses insurance are among the various types of landlord insurance.

In Europe, several online and offline landlord insurance businesses offer solutions for both residential and commercial properties. This landlord insurance migh...

The information presented here is collected via annual returns from Welsh social landlords on stock held by local authorities and registered social landlords (RSLs) as at 31 March each year and the associated average rents charged set on the same date for the following year. Stock The stock estimates in this dataset includes all stock owned, whether Welsh Government funded or otherwise as at 31 March each year, on which social rents are charged. It includes permanent and temporary stock. This dataset excludes: • properties that are charged at anything other than social rents, including those charged at intermediate or market rents, and intermediate tenures (for example shared ownership properties); • all non-residential properties; • dwellings leased to temporarily house the homeless; • any dwellings that are managed as a social lettings agency; • properties where the social landlord has sold the leasehold through right to buy but retains the freehold; and • RSL investment properties. The data were collected via the annual WHO15 returns from local authorities and annual RSL1 returns from RSLs up to 2008-09, but have since been collected via the Welsh Government Social Landlord Stock and Rents data collection. The proportion of social housing stock managed by RSLs will have been influenced by the large scale voluntary transfers of local authority stock. For further information please see the Quality Information in the accompanying Statistical Release (see weblinks). Within self-contained dwellings, the accommodation types include general needs, sheltered, other supported and extra care housing, and data are available on this basis back to 2008-09. During the 2012-13 data collection, the data collected for non self-contained dwellings were also broken down into the same accommodation types. Prior to that, non self-contained data were only collected as a total across all accommodation types. Stock figures will differ from dwelling stock estimates published, which assume that three bedspaces of a non-self contained unit is equivalent to one dwelling. Maisonettes are categorised as flats, whilst bungalows are categorised as houses. Data for English registered RSLs with stock in Wales is excluded. Rents This data presents information on the average weekly rents for wholly rented local authority and RSL dwellings set at the 31 March each year for the following financial year. The data were collected via the annual WHO15 returns from local authorities and annual RSL1 returns from RSLs up to 2008-09, but have since been collected via the Welsh Government Social Landlord Stock and Rents data collection. Rents are shown as at 31 March for the following financial year. If a local authority transfers its stock to a new RSL during the year, the rents are shown for the local authority for the whole of that year. In this dataset, the rents will move to the new RSL from the following 31 March. A list of the large scale voluntary transfers of local authority stock and dates of transfer can be found in the Quality Information in the accompanying Statistical Release (see weblinks). The average weekly rent is the average of the standard rent chargeable, before deduction for rent allowances and also excludes service charges or other charges for amenities (e.g. central heating, hot water supply or laundries) and water rates. Rents are based on a 52 week year. If rent free weeks are given the total amount payable is divided by 52. Properties of unusual size are assigned to the closest available category. Maisonettes are categorised as flats, whilst bungalows are classed as houses. The data includes secure as well as assured tenancies. Within self-contained dwellings, the accommodation types include general needs, sheltered, other supported and extra care housing, and data are available on this basis back to 2008-09. Rent data for non self-contained dwellings was collected for the first in 2012-13 and is broken down by the same accommodation types as self contained. Prior to 2012-13 no rent data is available for non-self contained dwellings.

The global housing rental service platform market is experiencing robust growth, driven by several key factors. The increasing urbanization and migration patterns worldwide are leading to a surge in demand for rental properties. Technological advancements, such as user-friendly mobile applications and improved online property listings, have significantly streamlined the rental process, making it more convenient and efficient for both landlords and tenants. Furthermore, the rise of the sharing economy and the increasing preference for flexible living arrangements are contributing to the market's expansion. This trend is further amplified by the growing adoption of smart home technologies, which enhance property management and tenant experience. We estimate the market size in 2025 to be approximately $15 billion, based on reasonable projections considering the rapid growth in similar online services and the expanding rental market globally. A Compound Annual Growth Rate (CAGR) of 15% is projected through 2033, indicating a substantial increase in market value over the forecast period. However, the market faces certain restraints. Competition among established players and new entrants is fierce, necessitating continuous innovation and strategic adaptation. Data security and privacy concerns regarding tenant and landlord information represent a significant challenge, requiring robust security measures. Regulatory changes and varying local laws across different regions add complexity to operations, potentially impacting profitability and expansion plans. Nevertheless, the market's overall growth trajectory remains positive, fueled by technological progress, changing lifestyle preferences, and the enduring need for efficient and transparent rental solutions. Key segments within the market, such as those focused on luxury rentals, short-term stays, or specialized niche markets (e.g., student housing) present lucrative opportunities for focused growth. Companies like Zillow, Trulia, and Apartment List, among many others, are actively shaping the market landscape with their diverse offerings and innovative features.

Percentage of Households: One Family: Couple with Dependent Children: Tenure & Landlord: Renter: State or Territory Housing Authority data was reported at 1.000 % in 2020. This records a decrease from the previous number of 1.200 % for 2018. Percentage of Households: One Family: Couple with Dependent Children: Tenure & Landlord: Renter: State or Territory Housing Authority data is updated yearly, averaging 1.400 % from Jun 2001 (Median) to 2020, with 11 observations. The data reached an all-time high of 2.400 % in 2001 and a record low of 0.700 % in 2016. Percentage of Households: One Family: Couple with Dependent Children: Tenure & Landlord: Renter: State or Territory Housing Authority data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H042: Survey of Income and Housing: Percentage of Households: by Tenure & Landlord.

Percentage of Households: One Family: One Parent with Dependent Children: Tenure & Landlord: Renter: Private Landlord data was reported at 47.300 % in 2020. This records an increase from the previous number of 46.700 % for 2018. Percentage of Households: One Family: One Parent with Dependent Children: Tenure & Landlord: Renter: Private Landlord data is updated yearly, averaging 43.200 % from Jun 2001 (Median) to 2020, with 11 observations. The data reached an all-time high of 48.000 % in 2016 and a record low of 37.800 % in 2004. Percentage of Households: One Family: One Parent with Dependent Children: Tenure & Landlord: Renter: Private Landlord data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H042: Survey of Income and Housing: Percentage of Households: by Tenure & Landlord.

The 2010 Private Landlord Survey is a national survey of landlords and managing agents who own and/or manage privately rented properties in England. The aim of the survey is to provide a snap-shot of the composition and experience of landlords and how they (together with any agent) acquire, let, manage and maintain privately rented accommodation. Source agency: Communities and Local Government Designation: Official Statistics not designated as National Statistics Language: English Alternative title: English Housing Survey Private Landlord Survey

Rental license tracking for condominiums, single-family homes, multi-family units and accessory apartments under Montgomery County Code, Chapter 29, Landlord-Tenant Regulations. This dataset is updated annually.

DOHLT Measure K Performance

Australia Percentage of Households: One Family: Other: Tenure & Landlord: Others data was reported at 1.600 % in 2020. This records a decrease from the previous number of 1.900 % for 2018. Australia Percentage of Households: One Family: Other: Tenure & Landlord: Others data is updated yearly, averaging 1.700 % from Jun 2001 (Median) to 2020, with 11 observations. The data reached an all-time high of 2.300 % in 2010 and a record low of 0.300 % in 2001. Australia Percentage of Households: One Family: Other: Tenure & Landlord: Others data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H042: Survey of Income and Housing: Percentage of Households: by Tenure & Landlord.

Amount charged by Registered Social Landlord (Private Registered Provider (PRP)) Average Weekly Rents for social housing.

Data is collected by the Housing Corporation via the annual Regulatory and Statistical Return (RSR) based on general needs stock only.

Figures are based on only the larger Registered Social Landlords (RSLs) completing the long form. Upto 2006 the threshold for completing the long form was that the RSL owned/ managed at least 250 units/bedspaces. From 2007 this increased to 1,000 units/bedspaces.

The districts, unitary authorities and counties listed above are based on 1 April 1998 boundaries. Figures for any 'new' re-organised areas have been estimated retrospectively applying the new boundaries back to 1997 and making appropriate assumptions.

Note that the average RSL rents within a local authority area can move down from one year to the next. This is especially true if, during the latest year, most of the LA stock has been transferred through a large-scale voluntary transfer to the RSL sector.

Larger housing associations report the rent they charge in the HCA’s Statistical Data Return.

Data in spreadsheet includes average weekly rents for housing association general needs properties by number of bedrooms, in London by borough (stock owned by larger associations only).

Australia Percentage of Households: Non Family: Group Households: Tenure & Landlord data was reported at 100.000 % in 2020. This stayed constant from the previous number of 100.000 % for 2018. Australia Percentage of Households: Non Family: Group Households: Tenure & Landlord data is updated yearly, averaging 100.000 % from Jun 2001 (Median) to 2020, with 11 observations. The data reached an all-time high of 100.000 % in 2020 and a record low of 100.000 % in 2020. Australia Percentage of Households: Non Family: Group Households: Tenure & Landlord data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H042: Survey of Income and Housing: Percentage of Households: by Tenure & Landlord.

The purpose of this project is to leverage the complementary technological skill, expertise, and organizational resources of the partnering organizations to create a database of eviction filings with the purpose of informing and improving the ability of Metro Atlanta policymakers, Non-government Organizations, service providers, tenant organizers, and government entities to understand and respond to eviction-related housing instability, particularly in the context of the COVID-19 pandemic. In addition, the intent of this project is to provide access to eviction filings data for research, practice, and policy purposes beyond the immediate threat of COVID-19. This partnership behind this project will collectively work to create the technology necessary to assemble the database of filings and make the filing information available to stakeholders in an understandable, accessible, secure, and responsible manner.About The DataThis data captures formal evictions activity in the metro Atlanta area as it is reflected in county court websites. This data does NOT reflect the number of rental households that undergo forced moves. Research has found that forced moves due to illegal evictions and informal evictions are far larger than the number of tenants displaced through the legal, formal eviction process. While eviction or dispossessory filings are evidence of housing instability, and constitute a negative event for tenants in and of themselves, they are not equivalent to displacement of a tenant. It is difficult to know whether a tenant leaves during a formal eviction process or at what stage of the process this occurs. Eviction filings initiate the process of eviction and are distinct from a "writ of possession" which grants a landlord the legal right to remove a tenant.This data is parsed once a week from the magistrate courts' case record search sites for Clayton, Cobb, DeKalb, Fulton and Gwinnett counties. Once the evictions case data is captured, each case is geocoded based on the defendant's address and the case events are analyzed to identify associated actions. Due to missing, incorrect, or difficult to parse addresses, approximately 1% of all filings are excluded from mapped totals. Analysis of case actions is done with an algorithm that is under development. For this reason, estimates of these actions are currently not included in the aggregated data presented in this tool. These estimates will, however, likely be included in future versions once the algorithm is complete and sufficiently validated. Additionally, due to ongoing improvements in the handling of parsing errors and the occasional lag in filings being entered into courts' online systems, counts will sometimes differ from those previously reported.TeamProject LeadElora Raymond, PhDAssistant ProfessorSchool of City and Regional PlanningGeorgia Institute of TechnologyProject LeadErik Woodworth, MA & MCRPResearch & Application Development CoordinatorData ScientistNeighborhood NexusAtlanta Regional Commission (ARC)Project LeadSarah Stein, JDResearch AdvisorCommunity & Economic DevelopmentFederal Reserve Bank of AtlantaData Acquisition & AnalysisVictor Pearse Haley, MCRPResearch AnalystCommunity & Economic DevelopmentFederal Reserve Bank of AtlantaData Storage & ProcessingGordon (Ge) Zhang, PhDResearch ScientistCenter for Spatial Planning Analytics & Visualization (CSPAV)Georgia Institute of TechnologyData Storage & ProcessingRama Sivakumar, MSSenior Research EngineerCenter for Spatial Planning Analytics & Visualization (CSPAV)Georgia Institute of TechnologyData Storage & ProcessingSubhro Guhathakurta, PhDChairSchool of City & Regional Planning (SCaRP)DirectorCenter for Spatial Planning Analytics & Visualization (CSPAV)Georgia Institute of TechnologyCourt Record Data SourcesFulton County Magistrates, State, and Superior Court Record SearchDeKalb County - Judicial Information SystemGwinnett County Courts - Tyler Odyssey PortalXerox CourtConnect Cobb Magistrate CourtClayton County Court Case InquiryOther Data SourcesUS Census Bureau, American Community Survey (ACS), 5-year estimates, 2014-2018ResourcesFAQ on National Eviction Moratorium provided by the National Low Income Housing Coalition (NLIHC)This page provides an explanation of the eviciton moratorium (effective Sept. 4th, 2020 to Dec. 31st, 2020) issued by the Center for Disease Control (CDC). It also provides a links to a number of resources including a downloadable Declaration of Eligibility (in multiple languages) to be completed, signed, and mailed by tenants to their landlord as the first step to invoking their right to the protections of this moratorium.CitationAny use of data downloaded from this site or reference to this work must be accompanied by one of the following citations.Data:Raymond, EL; Stein, S; Haley, V.; Woodworth, E; Zhang, G.; Siva, R; Guhathakurta, S. Metro Atlanta Evictions Data Collective Database: Version 1.0. School of City and Regional Planning: Georgia Institute of Technology, 2020, https://metroatlhousing.org/atlanta-region-eviction-tracker/.Methodology Report:Raymond, EL; Siva, R; Stein, S; Haley, V.; Woodworth, E; Zhang, G.; Siva, R; Guhathakurta, S. Metro Atlanta Evictions Data Collective Database: Version 1.0. School of City and Regional Planning: Georgia Institute of Technology, 2020, https://metroatlhousing.org/atlanta-region-eviction-tracker/.Data RequestsIf you or your organization would like access to data at a level of aggregation or format not available via the "Download Data" button on the tool, you will need to submit a formal request. Click below to begin the request process.https://docs.google.com/forms/d/e/1FAIpQLSexUZb9dXIx5h1GjaKmuNekxvp-CkgQ_qGsoAJXDERuLslSCg/viewform

• The CORE lettings data include information on the characteristics of both private registered providers and local authority new social housing tenants and the homes they rent. For each year, data is structured into four datasets based on type of letting (social rent general needs and supported needs, and affordable rent general needs and supported needs). It is a regulatory requirement for providers registered with the Homes and Communities Agency to supply the data. For those who are not registered, submissions are voluntary. Local authorities have participated in CORE since 2004-5 on a voluntary basis. Weighting is applied to adjust for non-response by local authorities for social rent datasets, and imputation is also carried out to address item-level non-response of key data on tenant characteristics for both local authorities and privately registered providers. The three datasets for affordable rent are not weighted or imputed.
• The CORE sales data include information on sales of local authority dwellings and some summary details on sales of registered provider stock (previously known as Registered Social Landlords or housing associations). Collecting these data allows for a better understanding of the socio-economic and demographic make-up of affordable housing customers and local housing markets and products. The sales dataset is imputed, with more information on the imputations within the data dictionary.

• For the standard End User Licence (EUL) version (SNs 9237 and 9238), the geographic level of the data is set at Government Office Region (GOR). Letting and voiding dates are provided at month and year only; age variables are top-coded at 90 years; income, benefits, earnings, charge and shortfall variables are banded to disguise unique values; landlords are grouped into coded categories.
• For the Special Licence access (SL) version (SNs 9239 and 9240), geographic level is set at Local Authority. The SL data have more restrictive access conditions than those made available under the standard EUL. Prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version.
• For Secure Access (SNs 9241 and 9242), the full CORE datasets are available, with some key variables recoded. Prospective users of the Secure Access version will need to fulfil additional requirements, including completion of face-to-face training and agreement to further stringent access conditions.

SN 9242: Continuous Recording of Social Housing Sales (CORE): Secure Access:

This study contains the Secure Access version of CORE Sales data only. The Secure Access CORE Lettings data are held under SN 9241.