Significant fluctuations are estimated for all segments over the forecast period for the sales channel distribution share. Only in the segment Online, a significant increase can be observed over the forecast period. In this segment, the sales channel distribution share exhibits a difference of 34.31 percent between 2019 and 2029. Find further statistics on other topics such as a comparison of the revenue in Croatia and a comparison of the sales channel distribution share in South Africa. The Statista Market Insights cover a broad range of additional markets.

Over the forecast period until 2029, the sales channel distribution share is forecast to exhibit fluctuations among the two segments. Only in the segment Online, a significant increase can be observed over the forecast period. In this segment, the sales channel distribution share exhibits a difference of 10.52 percent between 2019 and 2029. Find further statistics on other topics such as a comparison of the sales channel distribution share in Indonesia and a comparison of the sales channel distribution share in Singapore. The Statista Market Insights cover a broad range of additional markets.

In 2022, the majority of vacation rental sales worldwide were made online, at an estimated 69 percent. The share of global online vacation rental sales has seen steady growth since 2017, and this year-over-year increase is forecast to continue until 2027.

In 2023, the online sales channel of the vacation rentals industry in Malaysia dominated with 68 percent compared to 32 percent for the offline channel. Statista Market Insights forecasted that the share of the online channel for vacation rental sales will continue to increase and reach 80 percent by 2028, whereas the offline channel will decrease to 20 percent in the same year.

The global vacation rental website market is valued at US$ 1,482.6 Million in 2022. It is estimated to grow at a promising CAGR of 12.1% over the forecast period, reaching a value of US$ 4,640.2 Million by 2032.

Attribute	Details
Vacation Rental Website Size Value in 2022	US$ 1,482.6 Million
Vacation Rental Website Forecast Value in 2032	US$ 4,640.2 Million
Vacation Rental Website CAGR Global Growth Rate (2022 to 2032)	12.1%

Scope of Report

Attribute	Details
Forecast Period	2022 to 2032
Historical Data Available for	2017 to 2022
Market Analysis	US$ Million for Value and MT for Volume
Key Regions Covered	North America Latin America Europe The Middle East and Africa East Asia
Key Countries Covered	USA, Canada, Brazil, Mexico, Chile, Peru, Germany, United Kingdom, Spain, Italy, France, Russia, Poland, China, India, Japan, Australia, New Zealand, GCC Countries, North Africa, South Africa, and Turkey
Key Segments Covered	Property Type End User Region
Key Companies Profiled	BookingSync CiiRUS Kigo Inc. Virtual Resort Manager LiveRez OwnerRez 365Villas Convoyant 9flats Pvt Limited Airbnb Inc Booking Holdings Inc Expedia Group Hotelplan Holding AG MakeMyTrip Limited NOVASOL Oravel Stays Private Limited TripAdvisor Inc Rentalo Inc. Wyndham Destinations Inc World Travel Holdings Inc
Report Coverage	Market Forecast, Company Share Analysis, Competition Intelligence, Drivers, Restraints, Opportunities and Threats Analysis, Market Dynamics and Challenges, and Strategic Growth Initiatives
Customization & Pricing	Available upon Request

In 2024, more than twice the amount of vacation rentals in Germany were booked online, compared to booking offline. This was a significant development compared to earlier years in the timeline. Certain websites were especially popular among Germans searching for their home away from home while on vacation.

As per newly released data by Future Market Insights (FMI), the global vacation rentals market is estimated at US$ 74.8 billion in 2023 and is projected to reach US$ 132.7 billion by 2033, at a CAGR of 5.9% from 2023 to 2033.

Attributes	Details
Historical Value (2022)	US$ 74 billion
Current Year Value (2023)	US$ 74.8 billion
Expected Forecast Value (2033)	US$ 132.7 billion
Projected CAGR (2023 to 2033)	5.9%
2022 Value Share of North America in Global Market	24%
2022 Value Share of Europe in Global Market	19%

2018 to 2022 Global Vacation Rentals Market Outlook Compared to 2023 to 2033 Forecast

Historical CAGR (2018 to 2022)	5.4%
Forecasted CAGR (2023 to 2033)	5.9%

Country-wise Insights

Country	2022 Value Share in Global Market
United States	4.5%
Germany	3%
Japan	3.7%

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer 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. For more information about ACS layers, visit the FAQ. 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) 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.

Yearly Real Estate sales data by count and purchase price (median and average) from 2005 to 2018. All communities in the Keys to the Valley region are included.

Vermont Dataset Description

Purchase price - Average Sales Price based on listing price at time of purchase

Source – www.HousingData.org

NH Dataset Description

This data set provides an estimate of the median sale price of existing and new primary homes in New Hampshire. A primary home is defined as a single family home occupied by an owner household as their primary place of residence. Multi-family rental housing, seasonal or vacation homes and manufactured housing are not included in the analysis of this data.

Purchase price -
Median Sales Price

Data Collection Process - For the Period 1990 through 2014, the median purchase prices were calculated from data collected by the New Hampshire Department of Revenue Administration on the PA-34 Form through their vendor Real Data Corp. A PA-34 Form is filed by the buyer and seller at the time of sale of all real property in the State of New Hampshire. In 2015 this source of data was no longer available, and has been replaced by real estate transaction data supplied by The Warren Group and filtered and compiled by NHHFA. This change in data source is reflected in the charts by a break in the trend line.

Analysis - Median sale prices of all, new, existing, and condominium homes are calculated. The frequency of sales by $10,000 increment is also calculated for each of the above categories. Calculations based on sample sizes smaller than 50 are viewed as providing inconsistent and highly volatile results and are not typically released. Individual record level data is not released.

Limitations - The quality of this data at the higher geographic levels (statewide and counties) is consistent over the entire time series. For the larger LMAs and Municipalities the data is reasonably consistent with some holes in the data. For smaller LMAs and moderate sized municipalities the data is most consistent for existing homes since 1998. For the smallest municipalities this data set does not provide adequately consistent analysis.

Source - NHHFA Purchase Price Database; Source: 1990-2014 - NH Dept. of Revenue, PA-34 Dataset, Compiled by Real Data Corp. Filtered and analyzed by New Hampshire Housing.

https://www.nhhfa.org/publications-data/housing-and-demographic-data/

The revenue in the 'Vacation Rentals' segment of the travel & tourism market in the United States was forecast to continuously increase between 2024 and 2029 by in total 4.5 billion U.S. dollars (+22.2 percent). After the ninth consecutive increasing year, the revenue is estimated to reach 24.78 billion U.S. dollars and therefore a new peak in 2029. Notably, the revenue of the 'Vacation Rentals' segment of the travel & tourism market was continuously increasing over the past years.Find other key market indicators concerning the user penetration and number of users. The Statista Market Insights cover a broad range of additional markets.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer 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. For more information about ACS layers, visit the FAQ. 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) 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 holiday home market, valued at $665.9 million in 2025, is experiencing robust growth driven by several key factors. The increasing preference for unique and personalized travel experiences, coupled with the rising disposable incomes in developed and emerging economies, fuels demand for alternative accommodations beyond traditional hotels. The segment is diversified, encompassing various property types like castles, country houses, farmhouses, large barns, and luxury cottages, catering to diverse traveler preferences and budgets. The burgeoning popularity of eco-tourism and rural getaways further contributes to market expansion. Travel agencies and B&Bs are significant players in the distribution channel, leveraging their networks to reach a broader customer base. While data on the precise CAGR is unavailable, a conservative estimate, considering the aforementioned growth drivers, would place it between 5-7% annually for the forecast period (2025-2033). This growth is expected to be particularly pronounced in regions with strong tourism infrastructure and a growing middle class, such as North America and Europe. However, potential restraints include seasonal fluctuations in demand, the impact of global economic downturns, and the increasing competition from other short-term rental platforms. The market is witnessing significant technological advancements, with online booking platforms and property management systems streamlining the process for both homeowners and renters. Furthermore, the rise of sustainable tourism practices and a focus on environmentally friendly accommodations is influencing the design and management of holiday homes. The competitive landscape is fragmented, with several major players like Interhome Group and Hashtag Holiday Home LLC competing alongside numerous smaller, regional operators. Future growth will likely hinge on adapting to evolving traveler preferences, investing in technology, and embracing sustainable practices to maintain a competitive edge. Strategic partnerships with travel agencies and other tourism-related businesses will also play a crucial role in market penetration and expansion.

This layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state 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.This layer is symbolized to show the count and percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer 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. For more information about ACS layers, visit the FAQ. 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, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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.

New Home Sales in the United States increased to 676 Thousand units in February from 664 Thousand units in January of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

BR: FipeZap: House Asking Price Index: Sales: Niteroi data was reported at 130.333 Dec2011=100 in Feb 2025. This records an increase from the previous number of 129.681 Dec2011=100 for Jan 2025. BR: FipeZap: House Asking Price Index: Sales: Niteroi data is updated monthly, averaging 125.218 Dec2011=100 from Dec 2011 (Median) to Feb 2025, with 159 observations. The data reached an all-time high of 140.453 Dec2011=100 in Dec 2014 and a record low of 100.000 Dec2011=100 in Dec 2011. BR: FipeZap: House Asking Price Index: Sales: Niteroi data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB001: Real Estate: FipeZap House Asking Price Index: Sales. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources.

BR: FipeZap: House Asking Price Index: Rent: Rio de Janeiro: 2 Bedrooms data was reported at 210.032 2010=100 in Jan 2025. This records an increase from the previous number of 206.685 2010=100 for Dec 2024. BR: FipeZap: House Asking Price Index: Rent: Rio de Janeiro: 2 Bedrooms data is updated monthly, averaging 139.334 2010=100 from Jan 2008 (Median) to Jan 2025, with 205 observations. The data reached an all-time high of 210.032 2010=100 in Jan 2025 and a record low of 65.113 2010=100 in Jun 2008. BR: FipeZap: House Asking Price Index: Rent: Rio de Janeiro: 2 Bedrooms data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB005: Real Estate: FipeZap House Asking Price Index: Rent. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources.

Existing Home Sales in the United States increased to 4260 Thousand in February from 4090 Thousand in January of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

BR: FipeZap: House Asking Price Index: Sales: Praia Grande: 2 Bedrooms data was reported at 205.808 Dec2012=100 in Feb 2025. This records a decrease from the previous number of 207.439 Dec2012=100 for Jan 2025. BR: FipeZap: House Asking Price Index: Sales: Praia Grande: 2 Bedrooms data is updated monthly, averaging 130.064 Dec2012=100 from Dec 2012 (Median) to Feb 2025, with 147 observations. The data reached an all-time high of 207.705 Dec2012=100 in Dec 2024 and a record low of 100.000 Dec2012=100 in Dec 2012. BR: FipeZap: House Asking Price Index: Sales: Praia Grande: 2 Bedrooms data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB001: Real Estate: FipeZap House Asking Price Index: Sales. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources.

Brazil BR: FipeZap: House Asking Price Index: Sales: Broad: 1 Bedroom data was reported at 178.027 Jun2012=100 in Jan 2025. This records an increase from the previous number of 176.754 Jun2012=100 for Dec 2024. Brazil BR: FipeZap: House Asking Price Index: Sales: Broad: 1 Bedroom data is updated monthly, averaging 130.435 Jun2012=100 from Jan 2008 (Median) to Jan 2025, with 205 observations. The data reached an all-time high of 178.027 Jun2012=100 in Jan 2025 and a record low of 39.466 Jun2012=100 in Jan 2008. Brazil BR: FipeZap: House Asking Price Index: Sales: Broad: 1 Bedroom data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB001: Real Estate: FipeZap House Asking Price Index: Sales. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources. Broad Index: Is composed by 20 cities (São Paulo, Rio de Janeiro, Belo Horizonte, Distrito Federal, Salvador, Recife, Fortaleza, Santo André, São Bernardo do Campo, São Caetano do Sul, Niterói, Vitória, Vila Velha, Porto Alegre, Curitiba, Florianópolis, Goiânia, Campinas, Santos and Contagem) and the historical data started in 2012.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q4 2024 about sales, median, housing, and USA.