--- DATASET OVERVIEW --- This dataset captures detailed performance data for individual vacation rental properties, providing a complete picture of operational success metrics across different timeframes and market conditions. With weekly updates and four years of historical data, it enables both point-in-time analysis and long-term trend identification for property-level performance.

The data is derived from OTA platforms using advanced methodologies that capture listing, calendar and quote details. Our algorithms process this raw information to produce standardized and enriched performance metrics that facilitate accurate comparison across different property types, locations, and time periods. By leveraging our other datasets and machine learning models, we are able to accurately detect guest bookings, revenue generation, and occupancy patterns.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Property Characteristics: Property type, bedroom count, bathroom count, and capacity - Occupancy Metrics: Daily, weekly, and monthly occupancy rates based on actual bookings - Revenue Generation: Total revenue, average daily rate (ADR), and revenue per available day (RevPAR) - Booking Patterns: Lead time distribution, length of stay patterns, and booking frequency - Seasonality Indicators: Performance variations across seasons, months, and days of week - Competitive Positioning: Performance relative to similar properties in the same market - Historical and Forward Looking Trends: Year-over-year and month-over-month performance changes

--- USE CASES --- Property Performance Optimization: Property managers can leverage this dataset to evaluate the performance of individual listings against market benchmarks. By identifying properties that underperform relative to similar listings in the same area, managers can implement targeted improvements to pricing strategies, property amenities, or marketing approaches. The granular performance data enables precise identification of specific improvement opportunities at the individual property level.

Competitive Benchmarking: Property owners and managers can benchmark their listings against competitors with similar characteristics in the same market. The property-level performance metrics enable detailed comparison of occupancy rates, ADR, and revenue generation across comparable properties. This competitive intelligence helps identify realistic performance targets and market positioning opportunities.

Portfolio Optimization: Vacation rental portfolio managers can analyze performance variations across different property types and locations to optimize investment and management decisions. The dataset supports identification of high-performing property configurations and locations, enabling strategic portfolio development based on actual performance data rather than assumptions.

Seasonal Strategy Development: The historical performance data across different seasons enables development of targeted seasonal strategies. Property managers can analyze how different property types perform during specific seasons or events, informing marketing focus, pricing adjustments, and operational planning throughout the year.

Performance Forecasting: Historical performance patterns can be leveraged to develop accurate forecasts for future periods. By analyzing year-over-year trends and seasonal patterns, property managers can anticipate performance expectations and set realistic targets for occupancy and revenue generation.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market • Time Scales (daily, weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data processing methodology ensures high-quality, reliable performance metrics that accurately represent actual property performance. The raw booking and revenue data undergoes extensive validation and normalization processes to address inconsistencies, identify anomalies, and ensure comparability across different pro...

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Short Term Vacation Rental Market Size 2025-2029

The short term vacation rental market size is forecast to increase by USD 114.1 billion at a CAGR of 13.5% between 2024 and 2029.

The market is experiencing significant growth due to the expanding tourism industry and the increasing preference for flexible and affordable accommodation options. Technological advancements are revolutionizing the sector with online booking platforms, property management software, and smart home technology becoming the norm. However, inconsistency in providing quality vacation rentals remains a challenge. To enhance the guest experience, some rental properties are integrating spa and wellness facilities, while others are exploring the use of Augmented Reality to offer virtual tours. These trends reflect the industry's commitment to delivering superior guest experiences and meeting evolving traveler demands.

What will be the Size of the Short Term Vacation Rental Market During the Forecast Period?

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The short-term rental market, a segment of travel and tourism, has experienced significant growth in recent years, offering budget-friendly accommodations for both leisure and work travelers. With the rise of platforms like Airbnb and Booking.Com, this accommodation type has gained popularity among millennials and international travelers seeking unique, aesthetic stays. The market's size is substantial, with spending on services and goods in this sector continuing to increase. Emerging markets and low airfare prices have contributed to the market's expansion. Work-from-home trends have also driven demand for short-term rentals, allowing travelers to maintain productivity while enjoying eco-friendly and sustainable amenities.
Property owners benefit from the use of online booking platforms and property management software, streamlining the rental process. Technological trends, such as virtual tours, augmented reality, and innovative solutions, enhance the guest experience. The real estate industry has taken notice, with many investing in short-term rental properties. However, concerns regarding fake listings and safety remain, highlighting the need for continued industry regulation. Female visitors represent a significant portion of the market, with a focus on environmentally-friendly rentals and sustainable amenities becoming increasingly important. As the market continues to evolve, it is poised for continued growth and innovation.

How is this Short Term Vacation Rental Industry segmented and which is the largest segment?

The short term vacation rental industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Mode Of Booking

  Offline
  Online


Management

  Managed by owners
  Professionally managed


Type

  Apartments and condominiums
  Villas and luxury homes
  Cottages and cabins
  Resorts and bungalows
  Others


Geography

  Europe

    Germany
    UK
    France
    Italy


  North America

    Canada
    US


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Mode Of Booking Insights

The offline segment is estimated to witness significant growth during the forecast period. Offline segment had high demand previously when Internet penetration was not high, as word of mouth and repeat business were the most powerful factors for offline bookings. At present, some people are still hesitant to book their accommodation online. The main reason for this is people's lack of faith in online reservations. Another reason people choose to book short term vacation rentals offline is to ensure that they get the best rate. People generally think that by booking hotels offline, they will be able to negotiate with the staff or get extra discounts. Satisfied guests may become repeat customers, contributing to guest loyalty and positive word-of-mouth referrals. Thus, these factors will boost the growth of the offline segment and enhance the growth of the global short term vacation rental market during the forecast period.

Get a glance at the market report of share of various segments Request Free Sample

The Offline segment was valued at USD 87.10 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

Europe is estimated to contribute 32% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The European short-term vacation rental market is projected to expand due to the rising demand for travel and tourism, particularly for budget-friendly accommodations.

This dataset shows a listing of all short term rental properties actively registered with the City of Norfolk. A short term rental is either a vacation rental (not the owner’s primary residence) or homestay (the owner’s primary residence). It can be registered administratively with the City or by applying for a Conditional Use Permit (CUP). This dataset will be updated monthly.

NOTE TO USERS -- There may be disruption to this data set between March 19 to March 29 related to a upgrade. Please contact dsdopendata@austintexas.gov with questions.

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq

The general neighborhood and zip code location of active short term rentals (including type) across Austin, TX. Licenses are only active for one year. We have not included specific addresses, at the request of residents for safety reasons, but we have included street name and zip code. For more information or for records of licenses older than a year, concerned parties can pursue a public information request: public.information@austintexas.gov. FYI: your request will not be considered received unless it is sent to the proper address.

The global market for Vacation Rental Management Tools is projected to reach $216.6 million by 2033, expanding at a CAGR of 4.6% from 2025 to 2033. The growth of the market is primarily driven by the increasing popularity of vacation rentals and the rising number of vacation rental property owners worldwide. Other key factors contributing to market growth include the growing adoption of cloud-based solutions, the proliferation of mobile devices, and the need for efficient property management. The market is segmented based on application, type, and region. By application, the market is divided into SMEs and large enterprises. By type, the market is classified into cloud-based and on-premise solutions. Geographically, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America held the largest share in the global market in 2025 and is expected to continue its dominance throughout the forecast period. The Asia Pacific region is anticipated to witness the highest CAGR during the forecast period due to the increasing number of vacation rental properties in the region. The market is highly competitive, with a number of key players offering vacation rental management solutions. Some of the major companies in the market include BookingSync, CiiRUS, RealPage (Kigo), Hostaway, LiveRez, OwnerRez, 365Villas, Convoyant (ResNexus), AirGMS (iGMS), Avantio, Smoobu, Streamline, Lodgify, and Hostfully. Report Description This report provides an in-depth analysis of the vacation rental management tool (VRMT) market, covering market size, growth drivers, challenges, trends, and key players. With a global market size of over $10 billion, the VRMT market is poised to experience significant growth in the coming years, driven by the increasing popularity of vacation rentals and the growing number of property owners seeking professional management services.

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.

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

This dataset contains information about the status of Short Term Rental permits, locations of Short Term Rentals owners of the properties, and other relevant information. Short Term Rental permits within the Suburban Cities in Jefferson County are not included in this dataset.A short term rental is a dwelling unit that is rented, leased, or otherwise assigned for a tenancy of less than 30 consecutive days duration. All short term rental hosts must annually register each of their rentals with Louisville Metro's Office of Planning & Design Services; register with the Louisville Metro Revenue Commission (for tax purposes); and, if necessary, obtain Conditional Use Permits from the Louisville Metro Board of Zoning Adjustment. This data set contains information about the status of Short Term Rental permits, locations of Short Term Rentals owners of the properties, and other relevant information.For questions about this data, reach out to Planning & Design Services Amy.Brooks@louisvilleky.gov

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.

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

Country-wise Insights

The global Family Vacation Rental Management Tool market is a rapidly growing industry, with a projected value of $X million by 2033. This growth is being driven by a number of factors, including the increasing popularity of vacation rentals, the rise of the sharing economy, and the growing number of families traveling together. The market is also being supported by a number of technological advancements, such as the development of cloud-based software and mobile apps that make it easier to manage vacation rentals. Some of the key players in the Family Vacation Rental Management Tool market include BookingSync, CiiRUS, RealPage, Hostaway, LiveRez, OwnerRez, 365Villas, Convoyant, AirGMS, Avantio, Smoobu, Streamline, Lodgify, and Hostfully. These companies offer a variety of software and services that help property managers to manage their rentals, including reservation management, guest communication, and marketing. The market is also becoming increasingly fragmented, with a number of new entrants emerging in recent years.

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.

Scope of Report

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/

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

Short-term rentals are a type of lodging sometimes called vacation rentals. A house, condo, or apartment (or a part of one) that is rented for a fee for fewer than 30 consecutive nights is a short-term rental. Short-term rental operators are required to obtain an operator license & register each rental unit on that license. This dataset lists all Short-Term Rental operator licenses & their associated units. To learn more about short-term rental regulations in Seattle, please visit: https://www.seattle.gov/business-regulations/short-term-rentals

The Vacation Rental Cleaning Software market is experiencing robust growth as the short-term rental industry continues to expand, driven by an increase in travel demand and the proliferation of platforms such as Airbnb and Vrbo. This specialized software addresses the unique needs of vacation rental property manager

According to Cognitive Market Research, The Global Property Management Service market was estimated at USD 14.5 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 7.8% from 2023 to 2030. Rising Demands for SaaS-based Property Management Software to Expand Market Penetration

Subscription-based SaaS solutions benefit companies of all sizes. Businesses increasingly use SaaS solutions to optimize operations by automating workflows and removing manual input. Businesses can also lower the cost and complexity of on-premises deployment by installing SaaS solutions. SaaS software assists large multifamily property management organizations integrate several technologies across their portfolio. In addition, the SaaS model is crucial for multi-vendor device compatibility with legacy systems.

For instance, Planon collaborated with AddOnn in March 2021 to combine AddOnn's SaaS solution with Planon's software platform for building and service digitalization to provide end-to-end solutions to end-users worldwide.

(Source:planonsoftware.com/uk/news/planon-and-addonn-launch-partnership-with-introduction-of-mobile-cleaning-solution/)

Employees in real estate organizations rely on up-to-date information to make vital decisions. SaaS systems allow users to access information from any location and device with internet connectivity. A SaaS platform can help property managers link their property solutions with sophisticated payment services for quick and easy transactions.

Evolving Trends of Workforce Mobility to Strengthen Market Share

Many employees nowadays prefer to work from home rather than in offices, corporate headquarters, or a global company branch. This contributes to the need for flexible access to office resources and data. Besides, organizations are using virtual workplaces to reduce their physical infrastructure requirements to a bare minimum, allowing them to be more flexible and use their office space better. Many businesses seek mobility, workplace, and other integrated facility management solutions. This enables property managers to retain productivity while working with a huge crew. These solutions can be used by associated real estate agents & property managers to maintain track of all the properties they manage and the routine maintenance that needs to be performed on them. As a result, the rising trend of workplace mobility is propelling the property management service industry forward.

For instance, Entrata Inc. reported the integration of Alexa with residential buildings in April 2021. This integration would enable property managers to monitor or set up Alexa-enabled devices in each unit, allowing them to create voice-controlled automated homes.

(Source:www.prnewswire.com/news-releases/entrata-enables-alexa-experience-at-scale-with-amazons-alexa-for-residential-301263114.html)

Market Dynamics of Property Management Service

Integration Complexity and Data Security Concerns to Limit Market Growth

One significant restraint property management software services face is the complexity of integrating with existing systems and databases. Many property management companies already have established tools for accounting, tenant communication, maintenance tracking, and more. Implementing new software solutions can lead to compatibility challenges and difficulties in transferring data seamlessly. Furthermore, as property management software handles sensitive information such as tenant details, financial records, and property documents, ensuring robust data security becomes critical. Any breaches or unauthorized access can lead to legal consequences, financial losses, and company reputation damage.

Impact of COVID-19 on the Property Management Service Market

The COVID-19 pandemic significantly impacted the property management service market, introducing shifts in tenant behavior, remote work trends, and economic uncertainties that prompted property managers to adapt their strategies. Lockdowns and travel restrictions decreased demand for short-term rentals, while remote work trends increased the significance of property amenities and flexible leasing options. Property managers incorporated virtual tours, contactless services, and enhanced sanitation measures to address safety concerns. Moreover, the pandemic accelerated the adoption of proptech solutions for remote property monitoring and digital communication, reshap...

The Digital Vacation Rental Platforms market has experienced a remarkable transformation over the past decade, revolutionizing how travelers find accommodations and how property owners manage rentals. These platforms serve as intermediaries, connecting hosts with guests and providing a seamless way to browse, book,

This dataset contains FEMA applicant-level data for the Individuals and Households Program (IHP). All PII information has been removed. The _location is represented by county, city, and zip code. This dataset contains Individual Assistance (IA) applications from DR1439 (declared in 2002) to those declared over 30 days ago. The full data set is refreshed on an annual basis and refreshed weekly to update disasters declared in the last 18 months. This dataset includes all major disasters and includes only valid registrants (applied in a declared county, within the registration period, having damage due to the incident and damage within the incident period). Information about individual data elements and descriptions are listed in the metadata information within the dataset.rnValid registrants may be eligible for IA assistance, which is intended to meet basic needs and supplement disaster recovery efforts. IA assistance is not intended to return disaster-damaged property to its pre-disaster condition. Disaster damage to secondary or vacation homes does not qualify for IHP assistance.rnData comes from FEMA's National Emergency Management Information System (NEMIS) with raw, unedited, self-reported content and subject to a small percentage of human error.rnAny financial information is derived from NEMIS and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and application of business rules, this financial information may differ slightly from official publication on public websites such as usaspending.gov. This dataset is not intended to be used for any official federal reporting. rnCitation: The Agency’s preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnDue to the size of this file, tools other than a spreadsheet may be required to analyze, visualize, and manipulate the data. MS Excel will not be able to process files this large without data loss. It is recommended that a database (e.g., MS Access, MySQL, PostgreSQL, etc.) be used to store and manipulate data. Other programming tools such as R, Apache Spark, and Python can also be used to analyze and visualize data. Further, basic Linux/Unix tools can be used to manipulate, search, and modify large files.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.rnThis dataset is scheduled to be superceded by Valid Registrations Version 2 by early CY 2024.