Rent Inflation in the United States remained unchanged at 4 percent in April. This dataset includes a chart with historical data for the United States Rent Inflation.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to Apr 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Rent Inflation in the United Kingdom decreased to 6.30 percent in April from 7.20 percent in March of 2025. This dataset includes a chart with historical data for the United Kingdom Rent Inflation.

This table contains data described by the following dimensions (Not all combinations are available): Geography (247 items: Carbonear; Newfoundland and Labrador; Corner Brook; Newfoundland and Labrador; Grand Falls-Windsor; Newfoundland and Labrador; Gander; Newfoundland and Labrador ...), Type of structure (4 items: Apartment structures of three units and over; Apartment structures of six units and over; Row and apartment structures of three units and over; Row structures of three units and over ...), Type of unit (4 items: Two bedroom units; Three bedroom units; One bedroom units; Bachelor units ...).

Rent Inflation in Japan increased to 0.30 percent in April from 0.20 percent in March of 2025. This dataset includes a chart with historical data for Japan Rent Inflation.

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.

Price to Rent Ratio in the United States increased to 134.44 in the fourth quarter of 2024 from 133.75 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

Commercial rents services price index (CRSPI) by North American Industry Classification System (NAICS). Monthly data are available from January 2006 for the total index and from January 2019 for all other indexes. The table presents data for the most recent reference period and the last five periods. The base period for the index is (2019=100).

Autoscraping's Zillow USA Real Estate Data is a comprehensive and meticulously curated dataset that covers over 10 million property listings across the United States. This data product is designed to meet the needs of professionals across various sectors, including real estate investment, market analysis, urban planning, and academic research. Our dataset is unique in its depth, accuracy, and timeliness, ensuring that users have access to the most relevant and actionable information available.

What Makes Our Data Unique? The uniqueness of our data lies in its extensive coverage and the precision of the information provided. Each property listing is enriched with detailed attributes, including but not limited to, full addresses, asking prices, property types, number of bedrooms and bathrooms, lot size, and Zillow’s proprietary value and rent estimates. This level of detail allows users to perform in-depth analyses, make informed decisions, and gain a competitive edge in their respective fields.

Furthermore, our data is continually updated to reflect the latest market conditions, ensuring that users always have access to current and accurate information. We prioritize data quality, and each entry is carefully validated to maintain a high standard of accuracy, making this dataset one of the most reliable on the market.

Data Sourcing: The data is sourced directly from Zillow, one of the most trusted names in the real estate industry. By leveraging Zillow’s extensive real estate database, Autoscraping ensures that users receive data that is not only comprehensive but also highly reliable. Our proprietary scraping technology ensures that data is extracted efficiently and without errors, preserving the integrity and accuracy of the original source. Additionally, we implement strict data processing and validation protocols to filter out any inconsistencies or outdated information, further enhancing the quality of the dataset.

Primary Use-Cases and Vertical Applications: Autoscraping's Zillow USA Real Estate Data is versatile and can be applied across a variety of use cases and industries:

Real Estate Investment: Investors can use this data to identify lucrative opportunities, analyze market trends, and compare property values across different regions. The detailed pricing and valuation data allow for comprehensive due diligence and risk assessment.

Market Analysis: Market researchers can leverage this dataset to track real estate trends, evaluate the performance of different property types, and assess the impact of economic factors on property values. The dataset’s nationwide coverage makes it ideal for both local and national market studies.

Urban Planning and Development: Urban planners and developers can use the data to identify growth areas, plan new developments, and assess the demand for different property types in various regions. The detailed location data is particularly valuable for site selection and zoning analysis.

Academic Research: Universities and research institutions can utilize this data for studies on housing markets, urbanization, and socioeconomic trends. The comprehensive nature of the dataset allows for a wide range of academic applications.

Integration with Our Broader Data Offering: Autoscraping's Zillow USA Real Estate Data is part of our broader data portfolio, which includes various datasets focused on real estate, market trends, and consumer behavior. This dataset can be seamlessly integrated with our other offerings to provide a more holistic view of the market. For example, combining this data with our consumer demographic datasets can offer insights into the relationship between property values and demographic trends.

By choosing Autoscraping's data products, you gain access to a suite of complementary datasets that can be tailored to meet your specific needs. Whether you’re looking to gain a comprehensive understanding of the real estate market, identify new investment opportunities, or conduct advanced research, our data offerings are designed to provide you with the insights you need.

A. SUMMARY Beginning in 2022, the law requires owners of residential housing units in San Francisco to report certain information about their units to the San Francisco Rent Board on an annual basis. For units (other than condominium units) in buildings of 10 residential units or more, owners were required to begin reporting this information to the Rent Board by July 1, 2022, with updates due on March 1, 2023 and every March 1 thereafter. For condominium units and units in buildings with less than 10 residential units, reporting began on March 1, 2023 with updates due every March 1 thereafter. Owners are also required to inform the Rent Board within 30 days of any change in the name or business contact information of the owner or designated property manager. The Rent Board uses this information to create and maintain a “housing inventory” of all units in San Francisco that are subject to the Rent Ordinance.

B. HOW THE DATASET IS CREATED The Rent Board has developed a secure website portal that provides an interface for owners to submit the required information (The Housing Inventory). The Rent Board uses the information provided to generate reports and surveys, to investigate and analyze rents and vacancies, to monitor compliance with the Rent Ordinance, and to assist landlords and tenants and other City departments as needed. The Rent Board may not use the information to operate a “rental registry” within the meaning of California Civil Code Sections 1947.7 – 1947.8.

C. UPDATE PROCESS The Housing Inventory is continuously updated as it receives submissions from the public. The portal is available to the public 24/7. The Rent Board Staff also makes regular updates to the data during regular business hours, and the data is shared to DataSF every 24 hours.

D. HOW TO USE THIS DATASET It is important to note that this dataset contains information submitted by residential property owners and tenants. The Rent Board does not review or verify the accuracy of the data submitted. Please note that historical data is subject to change.

Notes for Analysis - Addresses have been anonymized to the block level - Latitude & Longitude are the closest mid-block point to the unit - Each row is a unit. To count total units, first select a year then count unique ids. Do not sum unit count.

This dataset provides values for RENT INFLATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Rent Inflation in Germany remained unchanged at 2.10 percent in April. This dataset includes a chart with historical data for Germany Rent Inflation.

The dataset contains current data on low rent and Section 8 units in PHA's administered by HUD. The Section 8 Rental Voucher Program increases affordable housing choices for very low-income households by allowing families to choose privately owned rental housing. Through the Section 8 Rental Voucher Program, the administering housing authority issues a voucher to an income-qualified household, which then finds a unit to rent. If the unit meets the Section 8 quality standards, the PHA then pays the landlord the amount equal to the difference between 30 percent of the tenant's adjusted income (or 10 percent of the gross income or the portion of welfare assistance designated for housing) and the PHA-determined payment standard for the area. The rent must be reasonable compared with similar unassisted units.

This table contains the Consumer Price Index (CPI). This index shows the price evolution of a package of goods and services that an average household in the Netherlands buys. The table also shows the derived consumer price index. This is the CPI exclusive influence of government measures such as VAT.

In addition to these indices, the table contains inflation. Inflation as an economic concept is the average price increase of the goods and services consumers buy. Inflation in the Netherlands is measured as the increase in the consumer price index (CPI) compared to the corresponding period in the previous year. The consumer price index shows the price evolution of a package of goods and services as purchased on average by Dutch households. The monthly-on-month development is also shown in the table. You can view these figures about 269 combinations of product groups. For each product group you can also find how much the Dutch consumer spends on it in relation to his total expenditure. This is called the weighting coefficient.

Data available from 1996 to 2015

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

Changes as of 18 May 2016 None, this table has been discontinued.

Changes as of 10 December 2015 As of 1 October, the national government has adjusted the points system for housing rentals. As a result, the rents of a limited number of homes have fallen, so the average rents also decreased. The effect of this rent decrease on the price indices of rent and imputed rent could not be determined earlier, as the housing corporations only announced the extent of the rent adjustments in November. The figures of the groups 04100 ‘Employable rent’ and 04200 ‘Accounted rental own home’ of October 2015 have therefore been adjusted.

The figures for the groups 061100 ‘Self-care medicines, 061200 ‘Other medical products’, 072200 ‘Autofuels’ and 083000 ‘Phone, fax and internet services’ have been updated from June to September 2015. This does not affect the published indices at main level.

The derived CPI has been revised down 0.01 index point over the month of August 2015.

When are new figures coming? This table is followed by consumer prices; price index 2015=100. See paragraph 3.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This database was created to accompany a report prepared by Joe Daniels, PhD, and Martine August, PhD, entitled “Acquisitions Programs for Affordable Housing: Creating non-market supply and preserving affordability with existing multi-family housing.” The database and report form part of the work performed under the HART project, and the report can be found at HART’s website: HART.ubc.ca. The database is a single table that summarizes 11 key elements, plus notes and references, of a growing list of policies from governments across the world. There are currently 108 policies included in the database. The authors expect to update this database with additional policies from time to time. The authors hope this database will serve as a resource for governments looking to become familiar with a variety of policies in order to help them evaluate what policies might be most applicable in their communities. Data Fields: List of data fields (15 total): 1. Government Order 2. Government Jurisdiction 3. Policy Name/Action 4. Acquisition Target 5. Years Active 6. Funder/Funding 7. Funding Amount (Program) 8. Funding Form 9. Affordability Standard 10. Affordability Term 11. Features/Requirements 12. Comments 13. Reference link 1 14. Reference link 2 15. Reference link 3 Description of data fields (15) 1. Government Order: - Categorizes the relative political authority in terms of one of three categories: Municipal (responsible for a city or small region), Provincial (responsible for multiple municipalities), or Country (responsible for multiple provinces; highest political authority). - This field may be used to help identify those policies most relevant to the reader. 2. Government Jurisdiction: - Indicates the name of the government. - For example, a country might be named “Canada,” a province might be named “Quebec,” and a municipality might be named “Calgary.” 3. Policy Name/Action: - Indicates the name of the policy. - This generally serves as the unique identifier for the record. However, there may be some programs that are only known by a common term; for example, “Right of First Refusal.” 4. Acquisition Target: - Describes the type of housing asset that the policy is concerned with. For example, acquiring land, acquiring existing rental buildings, renovating existing supportive housing. 5. Years Active: - The time period that the policy has been active. - Typically formatted as “[Year started] - [Year ended]”. If just a single year is listed (e.g. “2009”) that means the policy was only active that one year. - If the policy is active with no end date, then the format will be “[Year started] - ongoing.” If the policy has a specified end date in the future, that year will be listed instead: “[Year started] – [Expected final year].” 6. Funder/Funding: - The government, government agency, or organization responsible for the use of those funds made available through the policy. 7. Funding Amount (Program): - The dollar value of funds connected to the policy. - Sometimes this is the total value of funds available to the policy, and sometimes it is the actual value of funds that were used. - The funds indicated here do not necessarily correspond to the time period indicated in the ‘Years Active’ field. Additional detail will be added to clarify whenever possible. - If policy has “N/A” listed here, see ‘Features/Requirements’ for more information. 8. Funding Form: - Indicates the type of financial tools available to the policy. For example, “capital funding,” “forgivable loans,” or “rent supplements.” - If policy has “N/A” listed here, see ‘Features/Requirements’ for more information. 9. Affordability Standard: - Indicates whether the policy includes an explicit standard or benchmark of affordability that is used to guide or otherwise inform the policy’s goals. 10. Affordability Term: - Indicates whether the affordability standard applies to a specific time period. - This field may also contain other information on time periods that are relevant to the policy; for example, an operating loan guaranteed to be active for a specific number of years. 11. Features/Requirements: - Describes the broad objectives of the policy as well as any specific guidelines that the policy must follow. 12. Comments: - Author’s commentary on the policy. 13. Reference link 1: - A web address (URL) or citation indicating the source of the details on the policy. 14. Reference link 2: - A second web address (URL) or citation indicating the source of the details on the policy. 15. Reference link 3: - A third web address (URL) or citation indicating the source of the details on the policy. File list (1): 1. Property Acquisition Policy Database.xlsx

Description Ashtead (“Sunbelt”) is the second largest equipment rental company in the States, and cyclical fears plus a few minor operational missteps have created an attractive entry point into a secular winner. I also believe Sunbelt is under-earning to a larger degree than peers because of the organic nature of recent growth. Business Overview I'll keep this short because this and other equipment rental companies have been covered on VIC. Sunbelt buys and maintains a fleet of equipment including aerial work platforms (30% of fleet), forklifts (20%), earthmoving (14%), power and HVAC (11%) and more. Equipment is depreciated over 10 years (chosen to make equipment disposals breakeven at the low point of a cycle) and Sunbelt typically keeps it around for 7 years, getting more than 50% of original cost ("OEC" or original equipment cost) in rental revenue per year. After 7 years, equipment is disposed of at 40 cents on the dollar. Non-resi construction end markets are less than half of the business, and the rest is industrial, MRO and more. Renting equipment lets you get the exact right piece of equipment for a job. As an example, you used to find backhoes on jobsites much more, because a backhoe is the swiss army knife of earthmoving. That user might now prefer to rent either an excavator or a bucket loader, each of which peform half the function of the backhoe but in a more efficient manner. Rental also conserves capital, reduces the need for equipment yards/storage, solves logistics/ eliminates the need for vehicles that can move equipment, and solves the difficulty of maintaining owned equipment. Secular Trends The secular tailwinds come from both increased rental penetration as well as market share gains by the largest players. The use of rented equipment accounts for about 55% of the equipment market today and I expect it to hit at least 65% over time. Penetration is up from the low 40% range pre-GFC and single-digits in the 1990s. The top two players URI and Sunbelt have 15% and 11% share, respectively, and players smaller than the top 100 have 44% of the market. The top 10 players have grown market share from 20% in 2010 to about 45% today. The largest rental company businesses have improved over time. Scale gives purchasing economies with OEM suppliers, efficiencies in logistics and maintenance, and higher equipment utilization. URI and Sunbelt purchase equipment 15-20% cheaper than mom & pop operators. Moving heavy equipment to and from job sites requires a large fleet of dedicated vehicles. Equipment maintenance benefits from having expertise by equipment type, mechanic sharing and better utilization of parts and spares. In a typical branch, 6 out of 20 total employees might be mechanics. Utilization is measured both by time/physical utilization, which is just the amount of time the equipment is on rent, or by dollar utilization, which is measured by the rental revenue divided by the cost of the equipment (basically, asset turns). Dollar utilization is perhaps the most important metric, because it combines the time on rent and the rental rate. Dollar utilization is higher at the scale players for a large variety of reasons. More locations give larger players density and a higher likelihood that a given piece of equipment is needed by someone in that geography. It also lowers transportation costs and time and most importantly allows locations to share equipment. A better repair function means machines are on rent for longer and means that there is more equipment available to rent. A wider variety of equipment on rent also leads to higher rates. Sunbelt frequently mentions that they are not the lowest price, but they win business because of breadth, availability and service. The factors I’ve outlined above have led to stable dollar utilization, rising margins and thus rising returns on capital over time: Specialty rental equipment has become a larger part of Sunbelt’s mix over time. Specialty is a catch-all for equipment that can have more of a service component or more of a temporary, emergency, or one-off use case. When looking at historical results, note that specialty carries lower physical utilization but higher margins. Specialty equipment also depreciates more slowly and is generally less cyclical than general tool (i.e. non-specialty). Cyclical Factors Equipment rental is a cyclical business. Sunbelt will tell you that because equipment rental is now an essential part of customer’s businesses, rather than used as a top-up, future cycles will be more muted than the past. I mostly believe this for a few reasons. First, the large players are larger and more sophisticated. CEO Brendan Horgan likes to say that in the GFC they almost blindly lowered prices by 20% across the board without any pricing tools or great reason to do so. Second, the top 10 players are less leveraged. In the GFC, you not only had more leveraged companies, but some companies actually had covenants tied to...

Rent Inflation in Canada increased to 5.20 percent in April from 5.10 percent in March of 2025. This dataset includes a chart with historical data for Canada Rent Inflation.

Rent Inflation in Portugal decreased to 5.30 percent in April from 5.50 percent in March of 2025. This dataset includes a chart with historical data for Portugal Rent Inflation.