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

description: This list describes landlords and property owners who are designated "problem landlords". Landlords on this list have had two or more administrative hearing causes brought against them and were found liable or defaulted to one or more serious building violations.; abstract: This list describes landlords and property owners who are designated "problem landlords". Landlords on this list have had two or more administrative hearing causes brought against them and were found liable or defaulted to one or more serious building violations.

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.

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

DOHLT Measure K Performance

Australia Percentage of Households: One Family: Other: Tenure & Landlord: Renter data was reported at 26.300 % in 2020. This records a decrease from the previous number of 28.400 % for 2018. Australia Percentage of Households: One Family: Other: Tenure & Landlord: Renter data is updated yearly, averaging 22.700 % from Jun 2001 (Median) to 2020, with 11 observations. The data reached an all-time high of 28.400 % in 2018 and a record low of 11.700 % in 2003. Australia Percentage of Households: One Family: Other: Tenure & Landlord: Renter 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 data set is refreshed on a weekly basis on Fridays at 2:45 AM. The website will reflect the last time the data set was updated and the total count of rows. The grid on the “Data” tab will display the up to date data. However, in certain situations there is a delay in the refresh of the downloadable data file. Sometimes the downloadable file does not reflect the updates to the data in the portal. After a delay (duration has been variable; up to 30 minutes), the file will be updated on the server and then downloads will include the updated data.

The Private Rented Sector has grown considerably over the last 25 years and is now a crucial part of the UK's housing mix. The sector provides easily accessible accommodation for young, mobile, transient populations, but is increasingly being used to provide long term accommodation for vulnerable groups who in earlier times might have been able to access local authority or housing association accommodation. An online survey was selected as the principal data collection tool for the research. The resulting raw data has been attached as an SPSS Statistics Data Document.

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.

Australia Percentage of Households: Non Family: Lone Person: 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: Lone Person: 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: Lone Person: 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 Landlord Direct Rent market has witnessed significant evolution over the past decade, transforming how landlords and tenants interact in the rental space. This market facilitates direct connections between property owners and renters, allowing for simpler transactions and enhanced control over rental agreements.

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...

This table includes the average development of rent paid for dwellings (reduction, unchanged, increase) for social and other landlords.

Data available from: 2015

Status of the figures: The figures in this table are definitive.

Changes as of 4 September 2024: The figures of 2024 have been published.

When will new figures be published? New figures will become available in September 2025.

UAB "LANDLORD LT" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Legal & Resources - Corporate assets, safety, health and the environment

Landlord's Electricity Data contains data on electricity meter readings and billings.

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.

The global tenant management system (TMS) market is experiencing robust growth, driven by increasing demand for efficient property management solutions among landlords and property management companies. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based technologies offering scalability and accessibility, the increasing need for streamlined tenant communication and lease management, and the growing importance of data analytics for informed decision-making in property operations. Furthermore, the integration of features such as online rent payments, maintenance request tracking, and automated reporting contributes significantly to the market's appeal. While the precise market size for 2025 is unavailable, considering a plausible CAGR of 15% (a common rate for rapidly growing SaaS markets) and estimating a 2024 market size of $5 billion (a reasonable figure based on industry reports), we can project a 2025 market size of approximately $5.75 billion. This growth trajectory is anticipated to continue, driven by technological advancements and increasing market penetration across various property types and geographies. The market, however, faces some challenges, including the high initial investment cost for software implementation and the need for consistent staff training and adaptation to new technologies. Competition among established players and emerging startups also influences market dynamics. The competitive landscape is populated by both established players like Yardi and AppFolio, offering comprehensive solutions, and smaller, specialized firms focusing on niche markets. These companies continually innovate to improve their offerings, creating a dynamic market environment. Regional variations in market growth are expected, with North America and Europe likely maintaining significant market share due to high technology adoption rates and a larger base of property management companies. Asia-Pacific is projected to exhibit strong growth potential in the coming years, driven by increasing urbanization and growing demand for efficient property management solutions. The continued evolution of TMS platforms towards greater integration with other property management tools and enhanced data security features will further shape the market's trajectory throughout the forecast period of 2025-2033. Key segments within the market include residential, commercial, and multifamily property management, each with its unique demands and technological needs.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This is a report for all the relevant columns of DCA - Amount Allocated, Obligated, Paid- broken down by program, project, county and municipality.

As the renewable energy transition accelerates, housing, due to its high energy demand, can play a critical role in the clean energy shift. Specifically, multifamily housing provides a unique opportunity for solar photovoltaic (PV) system adoption, given the existing competing interests between landlords and tenants which has historically slowed this transition. To address this transition gap, this project identified and ranked Metropolitan Statistical Areas (MSAs) in the United States for ZNE Capital (the client) to acquire multifamily housing to install solar PV systems. The group identified seven criteria to determine favorable markets for rooftop solar PV on multifamily housing: landlord policy favorability, real estate market potential, CO2 abatement potential, electricity generation potential, solar installation internal rate of return, climate risk avoidance, and health costs associated with primary air pollutants. A total investment favorability score is calculated based on criteria importance assigned by the user. Investment favorability scores were investigated for different preferences to demonstrate the robustness and generalizability of the framework. The data analysis and criteria calculations were conducted using RStudio, ultimately to provide reproducible code to be used for future projects. The results are presented in a ranked list from best to worst metro areas to invest in. Future studies can utilize the reproducible code to inform decisions on where to invest in solar PV on multifamily housing anywhere in the United States by changing weights within the model depending on preferences. Methods

Collecting real estate and landlord data for metropolitan statistical areas (MSAs) from federal agency databases.

Real estate metrics: Six indicator metrics were selected to represent areas with growing housing demands. The metrics included were population growth, employment growth, average annual occupancy, annual rent change, the ratios of median annual rent to median income, and median income to median home price. The population estimates and median income data was downloaded from the Census Bureau. Median rent data was downloaded from HUDuser. Median home price data was downloaded from National Association of REALTORS®. Students were provided temporary memberships to Yardi Systems Matrix to obtain multifamily occupancy rates, and this data will not be redistributed. All the real estate metrics were combined into a single dataset using CBSA codes, which each MSA has a unique 5-digit identifier. Income-to-home price and rent-to-income ratios were calculated in R Studio.

Landlord data: the minimum security deposit and eviction notice data was collected for each state and manually compiled into an Excel. Security deposit information was provided as the number of months of rent. States with no maximum deposit limit received a score of 1.0, meaning it was the most favorable. Two month's rent was scored as 0.5, and one month's rent was given a score of 0.

Using NREL's REopt web tool to 1) model solar PV system on multifamily buildings in various cities and 2) obtain data to represent energy generation, CO2 abatement potential, avoided health costs from emissions, and solar project financial criteria.

An anchor city was identified within each MSA as the city with the highest population to input into the REopt tool. Default inputs were changed based on information provided by industry experts and changes in federal funding programs. Detailed instructions of inputs were created to ensure consistency when running the model for each city. The four outputs collected from the tool include: annual energy generation from renewables (%), lifecycle total CO2 emissions, health costs associated with primary air pollutants, and internal rate of return(%). The group divided up a list of cities, input the respective data for each one, obtained the outputs, then compiled it into a Google sheet. Outputs were checked by other members to ensure accuracy.

Collecting climate risk data from FEMA's National Risk Index Map.

Climate risk data was downloaded as a CSV file. The risk score was used to represent impacts of climate variability on long-term real estate investments. Risk scores were provided at the county level. The group identified the county each city resided in, to associate the correct score to each city in R Studio

Normalizing the data

Metrics were normalized by subtracting the minimum value for the metric from each value and dividing by the difference between the maximum and minimum values. This resulted in scores between 0 and 1 that were relative to the MSAs included in the analysis.

Weighing the data

Real Estate and Landlord Criteria metrics: these two criteria contained more than one metric, so the metrics within these criteria were weighted to produce real estate and landlord scores. Weights for each criterion sum to 1, in which higher weights indicate greater importance for multifamily real estate investments. Each weight was multiplied by the respective metric, then all weighted metrics within each criterion were summed to produce the criteria score. Investment Favorability Score: seven criteria were multiplied by respective weights based on the stakeholder's preferences. Weights sum to 1 to ensure consistency throughout the project. The sum of the seven weighted criteria is the investment favorability score.