Amsterdam is set to maintain its position as Europe's most expensive city for apartment rentals in 2025, with median costs reaching 2,500 euros per month for a furnished unit. This figure is double the rent in Prague and significantly higher than other major European capitals like Paris, Berlin, and Madrid. The stark difference in rental costs across European cities reflects broader economic trends, housing policies, and the complex interplay between supply and demand in urban centers. Factors driving rental costs across Europe The disparity in rental prices across European cities can be attributed to various factors. In countries like Switzerland, Germany, and Austria, a higher proportion of the population lives in rental housing. This trend contributes to increased demand and potentially higher living costs in these nations. Conversely, many Eastern and Southern European countries have homeownership rates exceeding 90 percent, which may help keep rental prices lower in those regions. Housing affordability and market dynamics The relationship between housing prices and rental rates varies significantly across Europe. As of 2024, countries like Turkey, Iceland, Portugal, and Hungary had the highest house price to rent ratio indices. This indicates a widening gap between property values and rental costs since 2015. The affordability of homeownership versus renting differs greatly among European nations, with some countries experiencing rapid increases in property values that outpace rental growth. These market dynamics influence rental costs and contribute to the diverse rental landscape observed across European cities.

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

Georeferenced Amsterdam Rental Values

Exploring Urban Housing Markets Through Historical Patterns

By [source]

About this dataset

This dataset provides insightful and comprehensive information on the spatial distribution of rental values in Amsterdam throughout a period of time. In order to generate this data, the Verponding registration from Amsterdam City Archives was consulted, which collected a tax known as the Verpondings-quohieren van den 8sten penning on the rental value of immovable property. This data was attained through transcribing and geo-referencing registration books from the archives; thereby incorporating both transcribed rental values of all real estate properties listed therein as well as geo-referenced tax records plotted onto an historical map of Amsterdam.

The compilation and analysis of historic rental values may offer further insights into underlying social, economic, and cultural developments within Amsterdam over time. Therefore, the potential applications for this dataset are enormous; offering investigators an opportunity to gather useful information with relation to urban renewal efforts or even supporting archaeological research studies. Moreover, with various columns such as order number, wijk district where applicable property is located within respective street name as well as details on whether said property is available for rent/own disposition - researchers may also utilize these collected metrics for meaningful planning/management decisions simultaneously unfolding hidden patterns concerning disparities or trends that might be discerned when compared to current trends employed by residents today

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How to use the dataset

This dataset provides insight into the spatial distribution of rental values in Amsterdam between 1647 and 1652. The data provided is a valuable resource for researchers looking to study the economic, social, and cultural history of Amsterdam over this period in time. With this data set, users can explore hidden patterns, disparities, and trends that may inform decision-making or help with urban renewal projects. Moreover, this dataset can also be used to assess archaeological and cultural heritage research.

In order to understand the georeferenced rental values better and draw meaningful conclusions from the data set it is important to keep few things in mind: - Check out handy columns such as ‘wijk’ (district) which offers information about where each property is located;
- The ‘rent/own’ indicates whether a property was rented (huur) or owned (koop);
- The ‘value’ column contains information regarding the rental value of each property; - The ‘tax’ column shows how much tax was paid on each listed property;
- In addition to this additional notes have been provided in some cases offering more insights into particular properties;

By seeing all these details together one will get an excellent overview of individual households renting or owning their real estate properties along with their tax payment throughout Amsterdam during this period 1647-1652. Additionally by graphing this data one could compare rental value against geographic location or even track consecutive years on how they vary year after year! This can help trace any historical changes taking place how they affect individual households within Amsterdam as well as socio-economic changes occurring throughout the city over the years!

Research Ideas

• Creating a predictive heat map by analyzing correlation between rental values and various other factors such as geographic location, proximity to public transportation, availability of amenities/services etc.
• Comparing and contrasting current maps of real estate prices in Amsterdam with the maps from this dataset to analyze shifts in property prices over time and understand the evolution of urban housing markets in the city.
• Studying socio-economic differences between different geographical areas based on rental values from this dataset, which could help provide a better understanding of the social, economic, and cultural history of the city

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permi...

A more recent version of these indicators can be found on this page: https://www.data.gouv.fr/fr/datasets/carte-des-loyers-indicateurs-de-loyers-dannonce-par-commune-en-2022/ Due to the evolution of the methodology and the communal mesh size, successive versions of the indicators cannot be compared to provide information on the evolution of rents. ### Context of the project Knowledge of the level of rents is important to ensure the proper functioning of the rental market and the conduct of national and local housing policies. The Directorate-General for Planning, Housing and Nature (DGALN) launched in 2018 the “rent map” project by partnering on the one hand with a research team in economics of Agrosup Dijon and the National Institute of Research in Agronomics (INRAE), and on the other hand with SeLoger, leboncoin and PAP. This innovative partnership has rebuilt a database with more than 9 million rental ads. On the basis of these data, the research team developed a methodology for estimating indicators, at the communal scale, of rent (including charges) per m² for apartments and houses. These experimental indicators are put online in order to be usable by all: state services, local authorities, real estate professionals, private donors and tenants. In a second phase of the project, the methodology will need to be consolidated and sustained, in order to provide for a regular update of these indicators. This project provides additional information to that offered by the Local Land Observatorys (OLL), deployed since 2013 and reinforced since 2018 by the Elan law. Today, this associative network of 30 OLL publishes every year precise information on the rents practiced in 51 of the main French agglomerations. ### Presentation of the dataset The data disseminated are indicators of ad rents, at the level of the municipality. The field covered is the whole of France, outside of Mayotte. The geography of the municipalities is the one in force on 1 January 2017. Rent indicators are calculated using ad data published on leboncoin, SeLoger and PAP over the period 2015-2019. Rent indicators are provided including charges for standard properties leased in the 3 rd quarter of 2018 with the following reference characteristics: — For an apartment: 49 m² and average area per room of 22.1 m² — For a house: 92 m² area and average area per room of 22.5 m² ### Data terms and conditions These indicators can be freely used, provided that the source is indicated as follows: “UMR 1041 CESAER estimates (AgroSup Dijon-INRAE) from SeLoger, leboncoin, PAP”. ### Precautions for use Rent indicators are calculated including charges, on ad data, so measure flow rents only. The data were duplicated but could not rely on very discriminating photos and characteristics. For municipalities with no housing leased through an advertisement on at least one of the three sites during the period considered, the rent indicator is that estimated for a larger grid comprising neighbouring municipalities with similar characteristics. Moreover, since the data do not make it possible to distinguish with certainty furnished and tourist rentals, biases in the rent indicators can be observed locally. Users are advised to consider rent indicators with caution in municipalities where the coefficient of determination (R2) is less than 0.5, the number of observations in the municipality is less than 30 or the prediction interval is very wide.

Rents in Germany continued to increase in all seven major cities in 2024. The average rent per square meter in Munich was approximately **** euros — the highest in the country. Conversely, Düsseldorf had the most affordable rent, at approximately **** euros per square meter. But how does renting compare to buying? According to the house price to rent ratio, house prices in Germany have risen faster than rents, making renting more affordable than buying. Affordability of housing in Germany In 2023, Germany was among the European countries with a relatively high house price to income ratio in Europe. The indicator compares the affordability of housing across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. Between 2012 and 2022, property prices in the country rose much faster than income, with the house price to income index peaking at *** index points at the beginning of 2022. Slower house price growth in the following years has led to the index declining, as incomes catch up. Nevertheless, homebuyers in 2024 faced significantly higher mortgage interest rates, contributing to a higher final cost. How much does buying a property in Germany cost? Just as with renting, Munich was the most expensive city for newly built apartments. In 2024, the cost per square meter in Munich was almost ***** euros pricier than in the runner-up city, Frankfurt. Detached and semi-detached houses are usually more expensive. The price gap between Munich and the second most expensive city, Stuttgart, was nearly ***** euros per square meter.

Abstract Purpose: The aim of this study is to quantify the effect of location on rental housing prices in the city of Athens. Theoretical framework: The right to adequate housing is a fundamental human right defended by democratic societies. Therefore, it is of interest to examine housing tenures for both owned and rented accommodation. Design/methodology/approach: Geostatistical methods (regression-kriging) were used to obtain the results, which are represented on an isovalue map of rental housing prices displaying the minor and major effects of location by zone. Findings: This study highlights the impact of location on rental housing prices by showing how the rent of a standard dwelling in the city of Athens varies depending on its location. Research Practical & Social implications: The main social implications of this work is it helps investors determine where to direct investments and it assists public authorities in deciding where to focus urban management policies, in order to control the undesirable effects of an excessive rise in rents caused by tourism. Originality/value: The main originality of this paper lies in its isovalue map of rental housing prices for standard dwellings, which can also be interpreted as a locational isovalue map.

The Aosta Valley region had the highest average rent for residential real estate in Italy in 2023. In October that year, the square meter rent in Aosta Valley amounted to 20.6 euros, almost eight euros above the national average. The regions of Lombardy and Tuscany followed with an average price amounting to 17.7 and 16.3 euros per square meter respectively. The average rent in Italy has increased notably since before the COVID-19 pandemic, when it was below 10 euros per square meter.

This dataset presents the reference rents by district, since the implementation of rent controls in 2019. It is updated each year on July 1 and the history of previous years is conserved.

Mapping of the neighborhoods where buildings are located, classified by geographical sectors and housing typology (Number of rooms, period of construction, type of rental (furnished, unfurnished)), specifying the reference rents, the increased reference rents and the reduced reference rents, expressed in euros per square meter of living space , across the entire territory of the City of Paris.

Rent control: how that works? On Paris.fr

This dataset corresponds to the content of the tool made available on Paris.fr "For all these questions: a single tool, the interactive map".

This dataset is based on that of Administrative districts

Attached to this dataset, you will find the orders annual.

The interactive map and the table of reference rents in this dataset have only an indicative value.

Rent references and enforceable maps of the prefectural decree are accessible on the website of the prefecture of the Ile-de-France region

Small Area Fair Market Rents (SAFMRs) are FMRs calculated for ZIP Codes within Metropolitan Areas. Small Area FMRs are required to be used to set Section 8 Housing Choice Voucher payment standards in areas designated by HUD (available here). Other Housing Agencies operating in non-designated metropolitan areas may opt-in to the use of Small Area FMRs. Furthermore, Small Area FMRs may be used as the basis for setting Exception Payment Standards – PHAs may set exception payment standards up to 110 percent of the Small Area FMR. PHAs administering Public Housing units may use Small Area FMRs as an alternative to metropolitan area-wide FMRs when calculating Flat Rents. Please See HUD’s Small Area FMR Final Rule for additional information regarding the uses of Small Area FMRs.Note that this service does not denote precise SAFMR geographies. Instead, the service utilizes a relationship class to associate the information for each SAFMR with the FMR areas that its ZCTA overlaps. For example, ZCTA 94558 overlaps the Santa Rosa, Napa, and Vallejo-Fairfield MSAs. Selecting that ZCTA will reveal the SAFMR information associated with each FMR area. To learn more about the Small Area Fair Market Rents visit: https://www.huduser.gov/portal/datasets/fmr/smallarea/index.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: Fiscal Year 2026Date Update: FY2026 : Oct. 1 - Sept. 30

This map shows the relationship between seniors who have worked in the last 12 months and are paying 30% or more on rental costs. Data is available in 5-year estimates at the state, county, and tract level for the entire US. The pop-up is configured to show:Total population 65+Percent of seniors paying 30%+ on rentPercent of seniors workingPercent of seniors below poverty levelThis map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. You can find the previous version of this map here.

Fair Market Rents (FMRs) represent the estimated amount (base rent + essential utilities) that a property in a given area typically rents for. The data is primarily used to determine payment standard amounts for the Housing Choice Voucher program; however, FMRs are also used to:Determine initial renewal rents for expiring project-based Section 8 contracts;Determine initial rents for housing assistance payment (HAP) contracts in the Moderate Rehabilitation Single Room Occupancy program (Mod Rehab), rent ceilings for rental units in both the HOME Investment Partnerships program and the Emergency Solution Grants (ESG) program;Calculate of maximum award amounts for Continuum of Care recipients and the maximum amount of rent a recipient may pay for property leased with Continuum of Care funds, and;Calculate flat rent amounts in Public Housing Units.Data is updated annualy in accordance with 42 USC 1437f which requires FMRs be posted at least 30 days before they are effective and that they are effective at the start of the federal fiscal year, October 1st.In order to calculate rents for units with more than four bedrooms, an extra 15% cost is added to the four bedroom unit value. The formula is to multiply the four bedroom rent by 1.15. For example, in FY21 the rent for a four bedroom unit in the El Centro, California Micropolitan Statistical Area is $1,444. The rent for a five bedroom unit would be $1,444 * 1.15 or $1,661. Each subsequent bedroom is an additional 15%. A six bedroom unit would be $1,444 * 1.3 or $1,877. These values are not included in the feature service.To learn more about Fair Market Rents visit: https://www.huduser.gov/portal/datasets/fmr.html/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Fair Market Rents Date of Coverage: FY2026 : Oct. 1 - Sept. 30

This EnviroAtlas data set depicts estimates for mean cash rent paid for land by farmers, sorted by county for irrigated cropland, non-irrigated cropland, and pasture by for most of the conterminous US. This data comes from national surveys which includes approximately 240,000 farms and applies to all crops. According to the USDA (U.S. Department of Agriculture) National Agricultural Statistics Service (NASS), these surveys do not include land rented for a share of the crop, on a fee per head, per pound of gain, by animal unit month (AUM), rented free of charge, or land that includes buildings such as barns. For each land use category with positive acres, respondents are given the option of reporting rent per acre or total dollars paid. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The median monthly rent for all apartment types in the U.S. has stabilized since 2022, despite some seasonal fluctuations. In August 2025, the monthly rent for a two-bedroom apartment amounted to ***** U.S. dollars. That was an increase from ***** U.S. dollars in January 2021, but a decline from the peak value of ***** U.S. dollars in August 2022. Where are the most expensive apartments in the U.S.? Apartment rents vary widely from state to state. To afford a two-bedroom apartment in California, for example, a renter needed to earn an average hourly wage of nearly ** U.S. dollars. This was approximately double the average wage in North Carolina and three times as much as the average wage in Arkansas. In fact, rental costs were considerably higher than the hourly minimum wage in all U.S. states. How did rents change in different states in the U.S.? In 2025, some of the most expensive states to rent an apartment only saw a moderate increase in rental prices. Nevertheless, rents increased in most states as of August 2025. In West Virginia, the annual rental growth was the highest, at ***** percent.

The dataset contains apartment sales and rent offers from the 15 largest cities in Poland (Warsaw, Lodz, Krakow, Wroclaw, Poznan, Gdansk, Szczecin, Bydgoszcz, Lublin, Katowice, Bialystok, Czestochowa). The data comes from local websites with apartments for sale. To fully capture the neighborhood of each apartment better, each offer was extended by data from the Open Street Map with distances to points of interest (POI). The data is collected monthly and covers a timespan between August 2023 and June 2024

Related work:

• Apartment for Sale in Poland - Data analysis
• Apartment for Sale in Poland - Deep Learning Model

Description

Files

• apartments_pl_YYYY_MM.csv - monthly snapshot of sell offers
• apartments_rent_pl_YYYY_MM.csv - a monthly snapshot of rent offers

Data Fields

• city - the name of the city where the property is located
• type - type of the building
• squareMeters - the size of the apartment in square meters
• rooms - number of rooms in the apartment
• floor / floorCount - the floor where the apartment is located and the total number of floors in the building
• buildYear - the year when the building was built
• latitude, longitude - geo coordinate of the property
• centreDistance - distance from the city centre in km
• poiCount - number of points of interest in 500m range from the apartment (schools, clinics, post offices, kindergartens, restaurants, colleges, pharmacies)
• [poiName]Distance - distance to the nearest point of interest (schools, clinics, post offices, kindergartens, restaurants, colleges, pharmacies)
• ownership - the type of property ownership
• condition - the condition of the apartment
• has[features] - whether the property has key features such as assigned parking space, balcony, elevator, security, storage room
• price - offer price in Polish Zloty
  • apartments_pl_YYYY_MM.csv: sale price
  • apartments_rent_pl_YYYY_MM.csv: monthly rent

This dataset and map service provides information on Fair Market Rents (FMRs). FMRs are primarily used to determine payment standard amounts for the Housing Choice Voucher program, initial renewal rents for some expiring project-based Section 8 contracts, initial rents for housing assistance payment (HAP) contracts in the Moderate Rehabilitation Single Room Occupancy program (Mod Rehab), and to serve as a rent ceiling in the HOME Investment Partnership Program (HOME) for rental assistance. HUD annually estimates FMRs for 530 metropolitan areas and 2,045 nonmetropolitan county FMR areas. By law the final FMRs for use in any Fiscal Year must be published and available for use at the start of that Fiscal Year, on October 1.

This scene visualizes advertised Tippecanoe county apartment rents collected from public listings within the past few years. These rents have been tied together with assessment data. Layers are filterable by categories like Effective Year Built, Tenancy Type, Amenity level, and more. The data in this map was known to include the most recent found rents and assessment data as of March 15, 2022.

The Balearic Islands, Catalonia, and the Community of Madrid were the most expensive Spanish regions for residential real estate rents in ************. The average monthly rent per square meter in these regions was **** euros. On the other end of the scale stood regions such as Extremadura and Castile-La Mancha which had the most affordable rental housing. In Spain, the majority of households live in an owner-occupied home. Nevertheless, rental rates have grown substantially since 2013, showing that the market is growing.

New Zonage “A/B/C” applicable from 01/10/2014 (Ministerial Decree of 01 August 2014).

The “A/B/C” zoning, created in 2003 at the time when Robien’s rental investment scheme was introduced, characterises the tension of the local real estate market, i.e. the adequacy of the demand for and the supply of available housing on a territory. It consists of five modalities ranging from the most tense (Abis) to the most relaxed (C).Franche-Comté is only affected by zones B2 and C. Several financial schemes use this zoning to determine the eligibility of territories for aid or to adjust their parameters (level of aid, ceiling of rents, etc.). These include the Intermediate Rental Investment Facility for Individuals (see Duflot Zoning), the Old Borloo, the Intermediate Rental Loan (PLI), the Zero Rate Loan (PTZ), the Social Accession Rental Loan (PSLA) and the Social Access Loan (PAS) to property, and the reduced rate VAT in the ANRU area.Some ANAH aid to social lenders is also linked to a ceiling on rent and the amount of resources of the tenant, which varies according to the zoning A/B/C. Following a consultation conducted by the Regional Prefect with the local authorities in the 4th quarter of 2013, the new zoning A/B/C was adopted by the Minister in charge of Housing on 1 August 2014. For Franche-Comté, 19 new municipalities were reclassified from C to B2, while no decommissioning was recorded. Its entry into force varies between 1 October 2014 and 1 February 2015 depending on the arrangements attached to it:

as of 1 October 2014 for: — the zero-rate loan; — the guarantee scheme of the FGAS; — the reduced rate VAT scheme for intermediate rental accommodation (279-0a A of the CGI); — the aid scheme for intermediate rental investment for private individuals (199 novitiies of the General Tax Code (CGI); — promises of sales of public land, pursuant to Article R. 3211-15 of the General Code of Ownership of Public Persons;

on 1 January 2015 for: — the benefit of aid from the National Housing Agency, the ‘old Borloo’ tax scheme; — the intermediate rental loan; — reduced VAT in ANRU area; — devices related to HLM promotion; — the assessment of resources for new intermediate dwellings held by HLML bodies in the context of their service of general economic interest;

as of 1 February 2015 for: — approvals of social loans for leasing-accession.

Data sources: order of the Minister of Housing dated 01 August 2014

Rent prices per square meter in the largest Dutch cities have been on an upward trend after a slight decline in 2020. Amsterdam remained the most expensive city to live in, averaging a monthly rent of 27.6 euros per square meter for residential real estate in the private rental sector. Monthly rents in Utrecht were around six euros cheaper per square meter. Both cities were above the average rent price of residential property in the Netherlands overall, whereas Rotterdam and The Hague were slightly below that. Buying versus renting, what do the Dutch prefer? The Netherlands is one of Europe’s leading countries when it comes to homeownership, having funded this with a mortgage. In 2023, around 60 percent of people living in the Netherlands were homeowners with a mortgage. This is because Dutch homeowners were able to for many years to deduct interest paid from pre-tax income (a system known in the Netherlands as hypotheekrenteaftrek). This resulted in the Netherlands having one of the largest mortgage debts across the European continent. Total mortgage debt of Dutch households reached a value of approximately 803 billion euros in 2023. Is the Dutch housing market overheating? There are several indicators for the Netherlands that allow to investigate whether the housing market is overheating or not. House price indices corrected for inflation in the Netherlands suggest, for example, that prices have declined since 2022. The Netherlands’ house-price-to-rent-ratio, on the other hand, has exceeded the pre-crisis level in 2019. These figures, however, are believed to be significantly higher for cities like Amsterdam, as it was suggested for a long time that the prices of owner-occupied houses were increasing faster than rents in the private rental sector.