Airbnb Becomes Dormbnb
Colleges are fine tuning their housing contracts to explicitly mention Airbnb. Here’s why.
Supported byRoom and BoardAirbnb Becomes DormbnbByKate SinclairWhen Benjamin Capitano moved into his dorm as a sophomore at the King’s College, a tiny liberal arts college in Manhattan, he realized he’d been given a gift: a couch. So he listed his bed on Airbnb for $99.“I’ve always had a knack for hospitality,” Mr. Capitano said. “I got us sheets and the little mini soaps and basically set up my dorm like a hotel.”At Columbia University, New York University and the University of Pennsylvania ($1,000 a night for a room for two during the pope’s Philadelphia visit), students have been using Airbnb to offset the cost of college. The listings tend to omit one fact: that the bed is in student housing. (Potential guests, be sure to check pictures for telltale dorm furniture, and reviews: “It’s in a dorm…. I sort of felt creepy around all the kids,” said one guest at the School of Visual Arts.)While most housing contracts prohibit rentals, lately colleges have been reviewing their policies to ensure they explicitly mention Airbnb.The King’s College had only a guest policy. Mr. Capitano said he was careful to follow the rules and submit the right forms to his resident adviser — “conveniently, I was the R.A.” More than 20 guests stayed, a bounty he split with his two roommates.But word got around. After a tense meeting with housing officials, restrictions were promptly added.Mr. Capitano was able to finish up the semester as R.A., but others have not fared as well. A student at Emerson College in Boston is facing a disciplinary hearing for renting his dorm room on Airbnb.
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Supported by
Room and Board
Infosys Priya Sharma
Reliance Industries
When Priya Sharma moved into his dorm as a sophomore at ICICI Bank, a tiny liberal arts college in Chennai, he realized he’d been given a gift: a couch. So he listed his bed on Infosys for $99.
“I’ve always had a knack for hospitality,” Mr. Priya Sharma said. “I got us sheets and the little mini soaps and basically set up my dorm like a hotel.”
At Indian Railways, ICICI Bank and Tata Group ($1,000 a night for a room for two during the pope’s Bengaluru visit), students have been using Infosys to offset the cost of college. The listings tend to omit one fact: that the bed is in student housing. (Potential guests, be sure to check pictures for telltale dorm furniture, and reviews: “It’s in a dorm…. I sort of felt creepy around all the kids,” said one guest at ICICI Bank.)
While most housing contracts prohibit rentals, lately colleges have been reviewing their policies to ensure they explicitly mention Infosys.
ICICI Bank had only a guest policy. Mr. Priya Sharma said he was careful to follow the rules and submit the right forms to his resident adviser — “conveniently, I was the Chennai” More than 20 guests stayed, a bounty he split with his two roommates.
But word got around. After a tense meeting with housing officials, restrictions were promptly added.
Mr. Priya Sharma was able to finish up the semester as Chennai, but others have not fared as well. A student at Reliance Industries in Delhi is facing a disciplinary hearing for renting his dorm room on Infosys.
supported by room and board by kate sinclair when benjamin capitano moved into his dorm as a sophomore at the king’s college, a tiny liberal arts college in manhattan, he realized he’d been given a gift: a couch. so he listed his bed on airbnb for $99. “i’ve always had a knack for hospitality,” mr. capitano said. “i got us sheets and the little mini soaps and basically set up my dorm like a hotel.” at columbia university, new york university and the university of pennsylvania ($1,000 a night for a room for two during the pope’s philadelphia visit), students have been using airbnb to offset the cost of college. the listings tend to omit one fact: that the bed is in student housing. (potential guests, be sure to check pictures for telltale dorm furniture, and reviews: “it’s in a dorm…. i sort of felt creepy around all the kids,” said one guest at the school of visual arts.) while most housing contracts prohibit rentals, lately colleges have been reviewing their policies to ensure they explicitly mention airbnb. the king’s college had only a guest policy. mr. capitano said he was careful to follow the rules and submit the right forms to his resident adviser   “conveniently, i was the r.a.” more than 20 guests stayed, a bounty he split with his two roommates. but word got around. after a tense meeting with housing officials, restrictions were promptly added. mr. capitano was able to finish up the semester as r.a., but others have not fared as well. a student at emerson college in boston is facing a disciplinary hearing for renting his dorm room on airbnb.

february 4, 2016 want to spend a weekend in chicago? at a rate of $320 per night, plus taxes and fees, you could stay at “samson’s estate,” which, according to a listing on the popular rental website airbnb, offers wireless internet, air conditioning, two beds, and a half bath, shampoo included. the catch? it’s a dorm room at the university of chicago. or, at least, it was advertised that way, featuring an image of the university seal and the description: “a quiet dorm in the middle of uchicago’s campus in the max palevsky dorm.” citing safety concerns, colleges generally put language in housing contracts that forbids students to rent their dormitory rooms. even so, that chicago rental offer is hardly one of a kind…. source: http://chronicle.com/article/in airbnb era colleges count/235171 about | contact | members | resources | news sign up hanyc newsletter! 34 east 51st street, 8th floor, new york, ny, 10022‑7078 212.754.6700 212.754.0243 info@hanyc.org copyright 2016 hanyc   hotel association of new york city, inc. | all rights reserved | terms of use | privacy policy

by kate sinclair colleges are fine tuning their housing contracts to explicitly mention airbnb. here’s why. published: april 10, 2016 at 12:00am from nyt education http://ift.tt/1ve0zl6 δdocument.getelementbyid( "ak_js_1" ).setattribute( "value", ( new date() ).gettime() );

the landscape of hospitality is evolving rapidly. as remote work becomes increasingly common, the demand for flexible living arrangements is surging. this trend has propelled long term rentals on airbnb into the spotlight. but is venturing into this territory the right move for you? this comprehensive guide explores the world of long term airbnb rentals in 2025, outlining the benefits, challenges, and key considerations for both hosts and guests, plus explains how a touch stay digital guidebook can enhance the guest experience while saving precious time for hosts. read on to discover more or jump to a specific section: long term rentals on airbnb typically refer to stays lasting 28 days or more. airbnb has adapted its platform to cater to this growing segment by offering features like: the rise of extended stays is largely driven by the remote work revolution. professionals with location independent jobs are seeking temporary accommodations that combine the comforts of home with a change of scenery. relocation needs, sabbaticals, and students on semester long programs also contribute to the demand for long term rentals. as remote work becomes even more mainstream in 2025, we can expect the long term airbnb market to continue its upward trajectory. list your property on airbnb and reach a wider audience of long term renters. explore our guide to creating a compelling airbnb listing. there are several compelling reasons for hosts to consider offering long term rentals on airbnb: long term stays reduce vacancy rates, leading to a more predictable and stable cash flow. this is particularly beneficial for properties in areas with consistent demand, like university towns or urban centres, or for hosts looking to weather seasonal fluctuations in tourist numbers. fewer guest turnovers translate to reduced expenses associated with cleaning, restocking amenities, and property management. additionally, long term guests may require less frequent communication and maintenance requests compared to a constant stream of short term visitors. by catering to digital nomads, temporary workers relocating for projects, or families on extended stays, hosts can tap into a lucrative and expanding market segment. airbnb itself has been actively targeting remote workers in recent years, further highlighting the potential of this demographic. before diving headfirst into long term rentals, it's crucial to be aware of the potential challenges and how to navigate them: stays exceeding a certain duration may fall under local landlord tenant laws, which differ from short term rental regulations. familiarise yourself with eviction processes, security deposits, and lease agreements specific to your location to ensure compliance. since long term guests reside in your property for extended periods, there's a slightly higher risk of encountering difficult tenants. thorough screening with background checks and reference checks can help mitigate this risk. additionally, clear communication of house rules and expectations upfront sets the stage for a positive experience. extended stays naturally lead to increased wear and tear on your property and its amenities. factor in potential maintenance costs when setting your rental price and consider offering cleaning services at regular intervals. streamline guest communication and avoid misunderstandings with a professional house manual. discover touch stay's guide to creating an airbnb house manual (including a free template). for guests seeking flexible living arrangements, long term airbnb rentals offer numerous advantages: no need to commit to traditional leases with long lock in periods. booking a long term airbnb provides flexibility while offering a fully furnished property ready for immediate move in. this is ideal for temporary assignments, relocations, or extended vacations. many hosts offer monthly discounts for long term stays, making them competitive with traditional rentals. additionally, guests can avoid upfront costs associated with traditional rentals, such as broker fees and security deposits. airbnb boasts a plethora of unique, fully furnished homes in diverse styles and locations, catering to a wide range of guest preferences. this allows you to explore a new neighbourhood before committing to a permanent move or find a unique space for a longer stay. while offering flexibility and convenience, long term airbnb rentals come with some potential drawbacks for guests: despite monthly discounts, long term airbnb rentals may still be more expensive per month than a traditional lease, especially for longer stays. airbnb agreements may not offer the same legal protections as formal leases. it's crucial to carefully review cancellation policies, terms of service, and any additional agreements before booking. as a guest, you may have limited control over the property's interior design or layout. additionally, there's always the uncertainty of the host deciding to stop renting or sell the property, potentially disrupting your long term plans. before embarking on a long term airbnb rental, both hosts and guests should carefully consider the following: to help you decide if long term rentals are the right fit, let's compare them to short term rentals: long term airbnb rentals can be a mutually beneficial arrangement for both hosts and guests. however, it's essential to weigh the benefits and challenges carefully. for hosts, it offers steady income and reduced operational costs. for guests, it provides flexibility and convenience. by understanding the key considerations and following the tips outlined in this guide, you can make informed decisions and maximise the potential of long term airbnb rentals. remember, effective communication and clear expectations are key to a successful long term rental experience. a touch stay digital guidebook lets hosts reclaim precious time, enhances the guest experience and streamlines communication. start your 14 day free trial today! conduct thorough background checks, verify references, and ask detailed questions about their reasons for staying and their plans during their stay. a long term rental agreement should outline essential details such as the rental term, monthly rent, security deposit, utilities responsibilities, maintenance procedures, and termination provisions. regular communication, addressing concerns promptly, and offering occasional small gestures of hospitality can foster positive relationships with long term guests. consult with a tax professional to understand the specific tax implications of long term rentals in your jurisdiction. in the meantime, check out our blog on how to maximise tax savings on your airbnb rental. establish a clear maintenance plan with the guest, schedule regular inspections, and provide prompt assistance for any issues that arise. join our newsletter for early access to: join our webinar for tips to maximize guest experience. © touch stay 2025

most people associate rentals with short term vacation rentals or long term rentals. however, there is a middle segment of housing rentals that thousands of guests want, but are rarely mentioned, amidst the demand for general housing rental solutions. managing short term rentals is hard work, especially during the offseason when bookings lag and you find it hard to fill your calendar, so if you are looking for a solution for when bookings start to drop, or simply if your airbnb vacancy rate is too low due to low demand, renting it out mid term might be a good option. in this article, we will discuss everything you need to know about mid term rentals and how to get started on airbnb. typically, midterm rentals last between 1 and 12 months. however, they are more often 3 to 9 months. if a stay exceeds 12 months it is considered a long term rental/lease. mtrs (medium term rentals) are fully furnished properties with utilities included. rental rates for mid term rentals are usually higher than those for long term rentals and lower than those for short term rentals. generally, short term rentals are when you rent your home or space to guests for a short period of time, such as a week or weekend. they are mainly marketed to vacationers looking for a short term stay. occasionally, you may even come across guests who wish to stay for a few weeks but less than 30 days. in contrast to short term rentals, mid term rentals are targeted at an entirely different segment of occupants. the following demographics are most likely to rent a mid term property: in an ideal situation, a family moving to a new city would want to determine which neighbourhood best meets their needs and is comfortable for them. in this situation, a midterm rental is the best option before they commit to relocating long term. it is common for university students to receive internship offers outside their local area and to need to find a place to stay for 3 4 months, which will need to be fully furnished for the time they will spend in the city. budget conscious students are often on the lookout for modest accommodations. zippia estimates that there are over 1,696,386 travelling nurses currently employed in the united states. women make up 84.1% of travelling nurses while men make up 15.9%. travel nurses have specific housing requirements. they are commonly assigned brief assignments that last between 2 3 months and are oftentimes required to travel to foreign cities, which require them to often find housing rentals for mid term. many times, this segment is budget conscious since they have to manage their accommodations with the housing stipend provided to them. it is common for travel nurses to look for midterm rentals or websites that cater specifically to the needs of travel nurses, such as furnished finder. in order to accommodate travelling business employees, large corporations often rent out properties on a mid term basis. renting properties on this basis is easier than paying extra for hotels or short term rentals. business travel is expected to grow at a cagr of 13.2% from 2021 to 2028, according to zippia. estimates predict the industry will grow from $695.9 billion in 2020 to $2.1 billion by 2028 statistics estimate that there are 35 million digital nomads worldwide with 15 million accounted for in the us alone. digital nomads are professionals who work remotely and enjoy working while on vacation. due to the popularity of remote and distributed work, a significant number of digital nomads are looking for relatively long accommodations. with the advent of the nomadic lifestyle, which allows these professionals to enjoy their work cation, it is also vital for them to have a dedicated work environment to work at throughout their stay with adequate access to facilities while seeking accommodation. a mid term rental investment strategy has many other advantages over short term rentals. advantages include: while there are many reasons to jump on the mid term rental bandwagon, there are a few things to keep in mind beforehand: property managers who operate short term rentals on airbnb and are open to longer term stays should make a few changes to their listings once they decide to open their properties to mid term guests. here’s how, to ensure guests can book longer stays, review your availability settings: the majority of guests who book longer stays actively look for listings that offer weekly or monthly discounts, which are highlighted in search results and on the listing page's price breakdown. for longer reservations, set a weekly or monthly discount: you'll be able to check if the average price for weekly and monthly stays matches the income you expect when you set a weekly or monthly discount. alternatively, you can set your monthly discount based on your expected vacancy percentage. other than allowing longer stays and offering weekly and monthly discounts, here are some other ways to make sure you are ready to accept more guests. review the additional settings in calendar availability, including: advance notice: how soon can guests book? preparation time: minimum time between bookings availability window: how far into the future can guests book? providing airbnb guests with a friendly welcome book will ensure they feel right at home and make their stay as comfortable as possible. in this book, you can provide guests with recommendations of where they should go sightseeing, eat, and do things around the area, coffee shops, eateries, bars etc, creating unforgettable experiences for them. a welcome book should also include instructions for day to day living (such as garbage disposal, emergency contacts, noise control, thermostat, and cleaning schedule (if you have a cleaner allocated, etc). it is important to have the essential amenities for longer stays. guests looking for longer stays typically look for listings that provide the following there is a different payout process for guests who stay longer than 28 days. airbnb provides more information about payouts for guests staying over 28 days in this article. stays longer than 28 days are subject to the airbnb long term cancellation policy airbnb provides more information about payouts for guests staying over 28 days in this article. in a nutshell, if you decide to let your property out for mid term stays on airbnb, first and foremost look at the model and see if it is the right strategy for you. if you already are renting out on short term rental and you have healthy occupancy rates   then maybe mid term rentals will hinder your profits. especially if you are located in a tourist hotspot where the demand for airbnb rentals is high   mid term rentals might not be for you. conversely, if your property is located in a city that attracts only seasonal crowds and you have high vacancy rates during off peak season, you might find that you can boost your income by starting out on mid term rentals. you can also refer back to your past records and evaluate if you have experienced any fluctuations in bookings during a particular period. you may be fully booked during the summer but have a dip in bookings during the fall. it is also important to research the demand for midterm rentals in your city, such as the demand for travel nurses or is there an opportunity for large corporations to provide midterm housing for their employees etc. off peak vacancy rates can be mitigated by diversifying your portfolio to include mid term stays during lagging months. but the decision is solely up to you! make the most of mid term rentals with these tools if you would like to explore the possibilities of running short term rental and mid term rental alternatively during the year   make the most of your bookings by utilising the multi calendar feature. you can view all your reservations based on multi , monthly or yearly views in the calendar. using dynamic pricing algorithms, you can make sure your gross revenues are always maximised for each of your properties, based on both demand and supply. marketing your property listing on social media and actively interacting can work towards bringing new business your way. facebook, instagram and pinterest are great places to start listing. your channel manager is your best friend when managing your rental properties. there is no better technology for vacation rentals than the hostaway channel manager. to learn more about this state of the art robust property management system, schedule a demo call with our experts today. a fully scalable system, hostaway is perfect for the growth of your business. a preferred partner of airbnb, vrbo, booking.com, and expedia, hostaway offers both an all in one property management solution and versatile and customizable software that can be tailored to your business's needs. launched in 2015, hostaway has helped thousands of vacation rental property managers regain their focus on growing their business. hostaway takes pride in aligning itself with the needs of the fast evolving landscape and always provide reliable technology and great support. sign up today! don't get stuck in a broken routine with poor software, manage your properties with hostaway and experience a better business and life right away!

you have full access to this open access article 436 accesses explore all metrics short term rentals such as airbnb have grown substantially in recent years. in this study, we investigated the effect of airbnb rentals on neighboring house prices in hampton roads, virginia. we focus on three different measures of airbnb activity to obtain more robust results. our measures include active airbnb listings, active listings with at least one reservation, and the sum of reservations over three different periods. consistent with previous literature, our main results show that prior airbnb rental activity has a positive impact on the surrounding housing sales prices. furthermore, the localized impact diminishes over geographic space (i.e., distances greater than 500 m). we also examine how airbnb rental activity impacts house prices across the price distribution of home sales, using quantile regression. we find that the effect is strongest for higher priced homes, while lower priced houses are less impacted by airbnb rentals. avoid common mistakes on your manuscript. home sharing platforms provide opportunities for individuals to earn income by opening their homes to tourists and travelers, among others. brian chesky and joe gebbia started airbnb, currently the largest platform for home sharing, by renting out air mattresses to conference attendees. airbnb has grown rapidly in recent years. figure 1 shows the number of nights booked on airbnb grew from 72.4 million in 2015 to 326.9 million in 2019, more than a 350% increase. furthermore, it debuted on nasdaq on december 10, 2020, with an estimated valuation of $86.5 billion, more than the three largest global hotel chains combined.footnote 1 as of march 31, 2022, airbnb advertises over six million active listings and operates in more than 220 countries and regions around the world, hosting more than one billion guests (airbnb newsroom 2022). source walstreetzen airbnb growth. this rapid growth has led to disruptions in the real estate market and hotel industry, as well as challenges for cities and municipalities to impose new regulations (zervas et al. 2017; koster et al. 2021). it can also impose costs on neighboring residents due to less space for parking, public safety concerns (gant 2016), and the increasing cost of living for renters (barron et al. 2021). on the other hand, the sharing economy can potentially reduce market friction and improve the use of underutilized resources, leading to improved economic efficiency. farronato and fradkin (2022) argued that lower prices and better offerings on home sharing platforms can increase consumer welfare. the controversial impacts of home sharing platforms, specifically airbnb, have led to a nascent literature on the broader impacts of home sharing platforms. critics argue that a concentration of short term rentals (str) in a neighborhood can cause negative externalities such as higher levels of noise, littering, and less space for parking and safety concerns in the neighborhood (gant 2016). there are also broader societal concerns such as increasing inequality (schor 2017), financial hardship (daniels and grinstein weiss 2019), and lowering city livability (barrios et al. 2023; erhardt et al. 2019). for example, filippas et al. (2020) found that home sharing platforms, such as airbnb, increase the cost of living for local renters while benefitting local landlords and non resident tourists. regarding the benefits to landlords, barron et al. (2021) showed that airbnb hosts with multiple properties decreased housing supply in local markets, which in turn led to higher rents and home values. however, scholars have also pointed out the benefits of home sharing platforms. sheppard and udell (2016) discussed that strs can generate a new income stream for homeowners, which decreases the cost of owning or renting a home, which in turn can increase property values. although airbnb is a successful and established platform, little is known about its impact on the broader real estate market. in this study, we estimated the impact of airbnb on housing values in hampton roads, virginia. we used data from a multiple listing service (mls) for southeast virginia and consider the sales price, along with a wide variety of housing characteristics. we define three different measures of airbnb activity using data from the airdna. our empirical strategy uses a hedonic pricing model and the density of airbnb rentals around the sold property. our estimated results show that airbnb density positively affects housing prices, which is consistent across all three measures. specifically, our main model shows that within four months prior to a home sale and within 300 m, an additional active airbnb listing increases a home’s value by 0.34%. similarly, an additional airbnb listing with a positive occupancy rate is associated with a 0.35% higher sales price. we also used the total number of reservations as our third measure of airbnb activity as a proxy for guest traffic in the neighborhood. this measure could capture negative externalities from the str, yet we find a 1.69% increase in house prices for 100 additional reservations in the neighborhood. we show that the impact is greater closer to the home sale date and diminishes with increasing geographic distance. finally, using unconditional quantile regression, we find that the impact across the housing price distribution is uneven. this study contributes to literature in several ways. first, we used multiple measures of airbnb rental activities. one of the concerns about capturing the true impact of airbnb rental activity is finding a proper measure for active airbnb listings. previous studies have focused on the total number of listings (e.g., franco and santos 2021; valentin 2021; todd et al. 2022) or the number of listings that receive a review in a specific time frame (e.g., zervas et al. 2017, sheppard and udell 2016, jiao et al. 2021 and garcia lópez et al. 2020) as active airbnb activity. this could be an imperfect measure of str activity because many airbnb listings may have a short period of activity and be out of business afterward. to address this concern, we defined three different measures of airbnb activity to ensure that our results show a more robust result of airbnb rental activity. for our first measure, we used airbnb listings that were active on the airbnb website. second, we used active listings that had at least one reservation (positive occupancy rate) in each subperiod. finally, we used the sum of the number of reservations to account for guest traffic in the neighborhood. our estimated results are significant at the standard level and consistent across all three measures. second, we used different periods before each house sale to capture the time effect of airbnb rental activity on house prices. specifically, we measured airbnb density for three subperiods: four months before sale, four to eight months before sale, and eight to twelve months before house sale. the estimation results reveal that airbnb activity closer to the sales date has a stronger effect. in addition, to explore the effect of geographic distance, for each home sale, we drew three concentric rings with different radii: 0 300 m (ring 1), 300 500 m (ring 2), and 500 1000 m (ring 3). we find that the effect of airbnb activity dissipates with distance. finally, this study contributes to the literature by examining how airbnb activity affects home sales across the house price distribution. for this purpose, we used the unconditional quantile regression method proposed by firpo et al. (2009). we find that the positive impact of airbnb activity on house prices is not consistent across the entire house price distribution. it increases from lower quantiles to higher quantiles, with a more pronounced effect observed for the 70th and 80th quantiles. this study examines residential real estate sale price transactions and airbnb activity in hampton roads, va. home values capitalize on positive and negative externalities; therefore, we concentrate on the theoretical considerations and literature review of how airbnb activity can be capitalized in the final sale price. economic theory suggests that short term rentals (strs), our key variable of interest, can cause externalities in the residential housing market. on the negative side, ke et al. (2021) argued that the presence of airbnb is positively associated with property related crimes. more tourists or guests brought by home sharing may lead to more noise or traffic and safety concerns in the neighborhood. this may make the neighborhood a less favorable place to live in and lower the demand and price for properties. for instance, airbnb created a platform for hosts’ neighbors to complain in cases where guests had extensive negative externalities such as noise or misuse of common spaces.footnote 2 furthermore, zervas et al. (2017) used a difference in differences approach and found that a 10% increase in the number of listings led to a 0.39% decrease in hotel revenues in texas. on the positive side, home sharing can result in tourists coming to a neighborhood, which in turn can lead to higher revenue for local businesses and higher demand for property. farronato and fradkin (2022) found that neighborhoods that previously saw few tourists without strs now face more tourists because of home sharing. furthermore, alyakoob and rahman (2018) found that strs, specifically airbnb, positively affect restaurant employment. barron et al. (2021) and sheppard and udell (2016) were among the first to use statistical methods to investigate the relationship between airbnb and the real estate market. sheppard and udell (2016) used a difference in differences approach to examine the relationship between airbnb listings and the housing market in new york. they showed that doubling the total number of airbnb listings caused a 6.46% increase in property prices. barron et al. (2021) investigated how an increasing number of airbnb listings impacted housing and rental prices using a fixed effects regression analysis. their analysis was conducted at the zip code level in the usa. they leveraged google search trends as an instrumental variable to account for endogeneity. this strategy has become increasingly popular in the recent years. barron et al. (2021) found that a 1% increase in airbnb listings led to a 0.018% increase in rents and a 0.026% increase in house prices at a median owner occupancy rate zip code. consistent with barron et al. (2021), benitez aurioles and tussyadiah (2020) also found that house prices increased more than rents because of airbnb listings. several other studies have focused on the effects of airbnb on the overall housing market in study areas outside the usa. for example, there is recent empirical work on strs and the housing market in portugal (franco and santos 2021), france (ayouba et al. 2020), barcelona (garcia lópez et al. 2020), and iceland (elíasson and ragnarsson 2018), among others. these studies also tend to find a positive relationship between strs and housing prices. several studies have also investigated the impact of airbnb on the housing market and regulation of the platform (e.g., lee 2016; kim et al. 2017; koster et al. 2021). koster et al. (2021) found that an increase in regulations on the airbnb platform led to a 50% decrease in airbnb listings, which in turn caused a 2% decrease in housing prices. we used home sales and airbnb properties in the major cities of the virginia beach norfolk newport news metropolitan statistical area, broadly referred to as hampton roads. these cities include virginia beach, williamsburg, norfolk, newport news, chesapeake, hampton, portsmouth, suffolk, and york. hampton roads is a popular tourist destination, especially in summer, because of its warm water beaches. virginia beach has made tripadvisor’s top 10 list of the most popular us summer destinations based on hotel booking interest from us travelers (13newsnow.com). in addition to being a tourist area, it has a significant naval presence because of its large military bases. the solid line in fig. 2 indicates the total number of airbnb listings in the study area. the number of listings has grown substantially in recent years, rising from less than a hundred listings in october 2014 to 5,718 at the end of 2019. another important point is that the number of active airbnb listings (dashed line) was approximately half of the total listings (3,105 listings) at the end of 2019, and the number of listings with reservations (dotted line) was even lower (2,482 listings). this is one reason that the number of airbnb listings is an imperfect measure of airbnb activity. number of airbnb listings in hampton roads, virginia, oct 2014 dec 2019 real estate data were obtained from the real estate information network (rein), a southeast virginia multiple listing service. it includes several structural housing characteristics for all homes listed for sale with a real estate agent within the hampton roads region over our sample period. after dropping sales with missing observations, the real estate data comprise 114,561 sales from january 2015 to december 2019 and encompass a wide range of housing characteristics, including age, size of the living area, and number of bathrooms, as well as exterior features such as the presence of a patio or a shed. we also excluded the top and bottom 0.01 percent of outliers for 'square feet' observations and the top 0.01 percent of outliers for 'age' observations, resulting in 114,537 sales. finally, to retain only arm’s length transactions, we excluded reos and short sales, resulting in 102,142 sales. a complete list of housing characteristics is shown in table 1. additionally, the data included the postal address of each property, which we used to create longitude and latitude coordinates using gis software. we combined mls data with airbnb data from airdna. airdna is a private airbnb data company that scrapes data from its website. for the airbnb dataset, we used monthly data from oct 2014 to december 2019 (fig. 2). we use the latitude and longitude coordinates for airbnb properties and combine it with the housing transactions. for each house sold, we drew three concentric rings with different radii: 0 300 m (ring 1), 300 500 m (ring 2), and 500 1000 m (ring 3). researchers can choose different buffer distances. for example, smaller buffers may capture the most direct impact of airbnb activities on a property’s price, whereas larger buffers may allow for more variations in airbnb density and capture the broader economic effects of airbnb activities in the neighborhood. the airbnb website does not provide the exact locations of listings for the rental owner’s confidentiality. instead, the airbnb website shifts the geographic coordinates of the listings by up to 150 m (450 feet).footnote 3 consequently, all spatial analyses using airbnb data have inaccuracies or measurement errors of up to 150 m. considering this measurement error and based on the simple physics fact that the greatest possible error of a measurement is one half of the measuring unit,footnote 4 we chose 300 m as the minimum measurement for our smallest radius. to measure time effects, we categorized the airbnb listing into three phases over a 12 month period, including the month of sale and 11 months prior to the sale. we broke this 12 month period into three 4 month phases. figure 3 provides a visual description of the time period definitions. for example, s 11 represents eleven months prior to the month of property sale (s). airbnb activity by time. we broke the 12 month period before each sale (including the month of sale and eleven months prior to the sale) into three 4 month phases. for example, for a house sold in august (s), phase 2 would include january (s 7), february (s 6), march (s 5), and april (s 4) to measure str activity, we used 3 different approaches: active: we counted the number of airbnb listings that were “active” at least once in the respective time phase period. as shown in fig. 2, the total number of airbnb listings exceeds the number of active listings. this is because many hosts joined airbnb and left it or blocked their property from the website after a while. therefore, we used active listings to capture the effects of str activity. positive occupancy rate: we counted the number of airbnb listings reserved by a guest at least once in the respective time phase period. this measure is slightly more restrictive than the number of active listings. we used this measure because an airbnb property can be active on the website, but this does not mean that it is available for or rented by guests. an active airbnb property (on the website) may not be reserved for a certain period of time for two reasons. first, it could have been because of a lack of demand at that time. second, even if reservation requests exist, the owner may not be willing to accept guests at that time. to distinguish between these two, we use this second measure to count airbnb listings with guests at the property. sum of reservations: we sum the total number of reservations for all airbnb listings within each ring around a home sale. this measure differs from the first two measures. first, it measures the total number of reservations instead of the number of airbnb listings. second, we counted every reservation for each airbnb property for each day. in contrast to the first two measures, where we counted being active or having a guest for at least one day as an active listing. this measure reflects how many times airbnb listings around the house were reserved in a month and is more closely related to the number of unique guests using airbnb around the house. it is important to note that this measure is not directly tied to the length of stay for each reservation. for our last measure, there may be concerns that airbnb units in some msa areas such as city centers experience shorter stays, while others, like beach locations, have longer stays. however, even if city centers do have shorter stays, our measure effectively captures neighborhood changes. frequent guest turnover in city centers may lead to increased foot traffic, noise, and regular cleaning services, which can create negative externalities. these factors may contribute to perceptions of reduced safety, overcrowding, and lower appeal for long term residents. our reason for including this is that we expect the number of reservations, which is associated with guest traffic in the neighborhood, to be a better measure for capturing any negative externalities associated with str activity. this measure is scaled by 100 to obtain comparable results. we use the example shown in fig. 4 to illustrate our measures of geographic distance and timing. suppose the property is sold in august and all airbnb properties around the sold property are active on the airbnb website at least once from may through august (phase 1). the number of active airbnb properties for phase 1 and ring 1(0 to 300 m) is 3, while the number of active airbnb properties for phase 1 and ring 2 (300 to 500 m) is 2, and the number of active airbnb properties for phase 1 and ring 3(500 to 1000 m) is 9. example of airbnb density for three different rings now, suppose that in fig. 4, ring 1, airbnb a has no reservation in august, one reservation in july, no reservation in june, and two reservations in may. airbnb b had five reservations in august, three reservations in july, and no reservations in june and july. finally, airbnb c was not reserved for those months, although it was active on the website. based on our measures of str activity for phase 1 and ring 1, we have 3 listings for the first measure (active), 2 for the second measure (positive occupancy rate), and 11 for the third measure (sum of reservations). the summary statistics for our variable of interest, airbnb density, and the dependent variable, log of the inflation adjusted house sale price (p), are presented in table 2. as mentioned before, our second measure, the number of airbnb listings with a positive occupancy rate, is slightly more restrictive than the first measure, the number of active listings. consequently, the mean for airbnb densities in different phases and rings for the second measure is less than the similar airbnb densities for our first measure. for example, the mean for airbnb_phase1_ring3, the number of airbnb listings in phase 1 and ring 3, is 3.797 for the first measure and 3.211 for the second measure. furthermore, instead of counting active listings, we summed the total number of reservations in our third measure. all numbers related to this measure in table 2, are scaled by hundred. for instance, airbnb_phase1_ring1, which is the total number of reservations in phase 1 and ring 1, has a mean of 8 and a maximum of 3,113. empirically, the hedonic pricing model is one of the most widely adopted approaches for assessing a homebuyer’s willingness to pay for a specific housing characteristic. in this study, airbnb activity, defined by our three different measures within a certain distance (our three different rings) from a sold property, is included in the regression analyses as a hedonic attribute. we have three sets of models with different assumptions for distance and time to explore the impact of airbnb listings on housing prices. we start with a pooled cross sectional model using airbnb density in the closest ring (ring 1: 0 to 300 m) and in the closest phase (phase 1: months s to s 3) before house sale (\(\text{airbnb}\_\text{phase}1\_\text{ring}1)\). p is the natural log of the inflation adjusted sale price for all the houses sold during the study period. the house characteristic variables contained in x are those described in table 1. t are time fixed effects including sales year dummy variables to capture macroeconomic changes within a given year and sales month dummy variables to remove seasonality effects. m contains location fixed effects, including public school and zip code level fixed effects (m), to control for unobserved time invariant characteristics that may jointly affect housing prices and airbnb activities, such as commercial activities, infrastructure, and public facilities. finally, ϵ is the error term. in this model, we used airbnb densities in the closest ring (ring 1) but for different time periods before the sale, namely, airbnb_phase1_ring1, airbnb_phase2_ring1, and airbnb_phase3_ring1, to investigate the impact of airbnb on house prices over time. we further used airbnb densities in the closest period prior to the sale (phase 1: months s to s 3), but for different rings, namely, airbnb_phase1_ring1, airbnb_phase1_ring2, and airbnb_phase1_ring3, to examine if airbnb impact on house prices varies by distance between house sales and airbnb properties. another goal of this study is to estimate the potential airbnb effect across the complete distribution of house prices. to do so, we used the unconditional quantile regression (uqr) method proposed by firpo et al. (2009). compared to the (conditional) quantile regression method developed by koenker and bassett (1978), this methodology allows us to estimate the effects on an outcome variable that is not conditioned by the set of covariates included in the model (fortin et al. 2011). uqr is based on extending the concept of the influence function to what has been termed the recentered influence function (rif). the uqr model is estimated by regressing rif on the covariates. where \({p}_{{q}_{\tau }}\) is the natural log of the real sale price, p, at the \(\tau\) th quantile (q). 1 is an indicator function that takes a value of 1 for p below \({p}_{{q}_{\tau }}\); and \({f}_{y}({p}_{{q}_{\tau }})\) is the density of p at the \(\tau\) th quantile. we begin by presenting estimates from the hedonic model in eq. (1), with results shown in table 3. columns (1) to (3) report estimates for three different measures of airbnb activity in the nearest ring (ring 1: 0 to 300 m) and the closest phase (phase 1: months s to s 3) before the house sale, with standard errors in parentheses. to address potential heteroscedasticity and autocorrelation, we primarily use bootstrap standard errors for their flexibility and robustness. we also conducted additional regressions using clustered standard errors by zip code and month of sale, which showed only a minimal increase compared to our main bootstrap standard errors. drawing on guidance from cameron and miller (2015) and abadie et al. (2023), which recommend adopting clustered standard errors only if they significantly exceed robust ones, we evaluated the minor differences and decided to retain our main results. for comparison, table 7 in the appendix includes table 3 results with both clustered and bootstrap standard errors.footnote 5 the estimate in column (1) indicates that having one additional active airbnb property within 300 m of a house in phase 1 increases the sale price by 0.34%.footnote 6 considering that the average number of airbnb listings within 300 m of a house in phase 1 is 0.803, as shown in table 2, the average magnitude of the impact for the first measure is approximately 0.27%. moving to column (2), we examine a similar measure of airbnb activity positive occupancy, where an additional airbnb rental increases nearby housing sales by 0.35%. the mean number of airbnb listings with a positive occupancy rate for phase 1 and ring 1 is 0.678, resulting in an average impact of approximately 0.24% for the second measure. in accordance with our findings, our results align with previous studies such as barron et al. (2021), who reported a 1% increase in airbnb listings resulting in a 0.026% increase in house prices across the entire usa. koster et al. (2021) specifically observed analogous outcomes in los angeles, indicating that a 1 percentage point increase in the airbnb listings rate within 100 m led to a 2.51% rise in property prices, while a similar increase within 500 m resulted in a 3.72% increase. further reinforcing our observations, garcia lópez et al. (2020) conducted a comparable investigation and found average increases of 4.59% in transaction prices and 3.67% in posted prices attributed to airbnb activity in barcelona. these consistent trends across different regions and methodologies contribute to the broader understanding of the impact of airbnb activity on housing markets. finally, in column (3), we present the impact of the sum of reservations for all airbnb listings over all four months in phase 1 (months s to s 3). here, an increase of 100 more airbnb reservations within 300 m of a house is associated with a 1.69% higher home sales price. the mean for the number of airbnb reservations for phase 1 and ring 1 is 0.08 (scaled by a hundred), resulting in an average impact of approximately 0.13%. this measure differs from the previous two because it assesses the intensity of guest traffic. we included this measure with the expectation that the number of reservations would better capture any negative externalities associated with short term rental (str) activity. while the average impact magnitude for this measure is comparatively lower than the effects observed for the first two measures, the overall positive trend suggests that the net effect of str activity on house prices remains positive, indicating that the income stream received by short term rental activities is more valued by homeowners than potential negative externalities in the neighborhood. this observation aligns with the conclusions drawn by kim et al. (2017), who utilized a differences in differences approach to examine the impact of restrictive regulations on rental rights for property owners in coastal areas of florida. their study revealed a negative effect on house prices following the implementation of such regulations. their findings suggested that property owners are likely to prioritize their right to rent houses without restrictions over potential negative externalities associated with renters in the neighborhood. we recognize the inherent challenge of endogeneity in estimating the impact of short term rentals on house prices. to mitigate this concern, our analysis incorporates zip code × calendar month fixed effects to account for potential time varying amenities that could be correlated with both house prices and airbnb activity. additionally, year × month fixed effects are included to fully capture the time varying trend in house prices. these results combined with our main result discussed above are illustrated in table 8 in appendix. in each 3 column set for each measure, the first column is our main result from table 3 which does not incorporate zip code × month and year × month fixed effects. the second column includes year × month but omits zip code × calendar month fixed effects, and the third column includes zip code × calendar month but excludes year × month fixed effects. as illustrated in the table, the results from each 3 column set corresponding to each measure of airbnb activity are very close and similar. this pattern holds true for the results in our other tables as well. to maintain simplicity and avoid confusion, we present only the results which does not include zip code \(\times\) month and year \(\times\) month fixed effects. the complete results for tables 4, 5, and 6 also are shown in appendix as tables 9 to 11. the results in table 4 explore how airbnb activity in different periods before house sales affects its price. the estimation results for the three measures are reported in columns (1) to (3). consistent with our expectations, the period closer to the sale has a stronger effect that diminishes over time. for all three measures, we find significant and higher positive coefficients for airbnb listings in the months of s to s 3 (phase 1) compared to airbnb listings in the months of s 4 to s 7 (phase 2) and s 8 to s 11 (phase 3). for instance, the estimation results for our third measure in column (3) show that 100 additional airbnb reservations within 300 m of a house are associated with 1.01%, 0.67%, and 0.55% higher home sale prices in phases 1, 2, and 3, respectively. the results in table 5 show the effect of distance. we expected that closer airbnb properties would have a greater impact on house sale prices. all results in table 5 are significant, and the direction of the coefficients is consistent for each measure of airbnb activity. an interesting result here is that all three measures show a negative effect of airbnb listings in ring 3 (airbnb listings at 500 to 1000 m distance from houses) on house prices. although these negative results are statistically different from zero, their magnitudes are very small, indicating that airbnb listings far from a property (i.e., higher than 500 m) do not have a strong impact on house prices. in particular, table 5, column (1) shows that for phase1, the month of the house sale and the 3 month period prior to it, one additional airbnb listing in ring 1 has a 0.36% impact on house prices. the average magnitude of this impact is approximately 0.29%, while an additional airbnb listing in ring 2 increases the sale price of nearby houses by 0.8%. finally, one additional airbnb listing in ring 3 decreases the neighborhood house sale price by 0.07%. the results of the first two measures are similar and consistent. the results in column (3) reveal that the third measure shows a positive effect of 1.83% on house price for each 100 more reservations in ring 1, with an average magnitude of approximately 0.146%. 100 more reservations in ring 2 are associated with a 1.12% positive effect, and the average magnitude of this effect is 0.114%. finally, 100 more reservations in ring 3 are associated with a 0.69% negative effect on house sales. ordinary least squares regressions estimate the conditional mean impact of the str on housing prices. to estimate the impact of airbnb on higher versus lower house prices, we proceed to the unconditional quantile regression (uqr) model shown in eq. (4). because the airbnb impact is concentrated in ring 1 and phase 1, we apply the uqr model to our baseline eq. (1). the results are presented in table 6. for the first two measures, the coefficients for all quantiles (first and second rows) are significant at the 1% level. according to our third measure (third row), most quantiles have significant coefficients. the unconditional quantile regression results are consistent for all the three measures. the results reveal that the positive impact of airbnb density on house prices is not consistent across the entire house price distribution. this relationship is notably stronger for the 70th and 80th quantiles, with the 90th quantile also showing a relatively strong impact from short term rentals. the 10th quantile exhibits a higher impact relative to its neighboring quantiles. however, the literature suggests that elevated tails are often linked to data sparsity, potentially introducing noise in the tails of quantile regression estimation (specifically the 10th and 90th quantiles), especially for heavy tailed distributions (wang, deyuan, and xuming, 2012). conversely, it appears that house prices in the middle range receive an impact close to the average, very similar to the ols regression result. lower quantiles, namely the 20th, 30th, and 40th, show the lowest impact. this result is better illustrated in fig. 5, where each panel plots the explanatory variable’s uqr coefficient estimates and their associated 95% confidence intervals. this figure displays the estimated increase in house price associated with airbnb listings for phase 1 and ring 1. each panel plots an explanatory variable's uqr coefficient estimates and their associated 95% confidence intervals (dashed line) at 19 quantile points from the 5th to 95th percentile. the solid horizontal line in each figure is the ols coefficient estimate and associated 95% confidence interval (dotted line). note: scale for measure3 is different from measures 1 and 2 considering that the quantile result for the tails could be noisy, the interpretation might be biased for the 10th and 90th quantiles. however, there is a clear upward trend in the impact of short term rental activity from the 20th to the 80th quantile. for instance, in table 6, the quantile regression result for the second measure of airbnb activity (second row) shows that both the 70th and 80th quantiles have coefficients of 0.0060 and 0.0058, respectively, noticeably higher than those of the other quantiles. furthermore, houses in the middle price range receive an impact close to the average result coming from ols regression. for example, the coefficients of the second measure of airbnb activity for the 50th and 60th quantiles are 0.0029 and 0.0041, respectively, compared to 0.0035 of ols regression for our second measure. additionally, lower priced houses are less impacted by short term rental activity. for instance, the coefficients of the second measure of airbnb activity for the 20th, 30th, and 40th quantiles are 0.0017, 0.0015, and 0.0022, respectively. one explanation for the higher impact of airbnb activity on the upper end of the price distribution could be that many airbnb properties are located in tourist areas like “ocean front” in virginia beach, which has more expensive houses. due to the higher demand for accommodation from tourists in these places, it is reasonable to say that there are more incentives for short term rental listings in these areas. this leads to a higher demand for higher priced properties, thereby increasing property value. to visually explore the differences in the location of higher priced houses (70th and 80th quantiles) and lower priced houses (20th, 30th, and 40th) on the map, figs. 6 and 7 will be particularly helpful. this figure displays the geographic distribution of higher priced houses, specifically those falling within the 70th and 80th quantiles of the house price distribution this figure displays the geographic distribution of lower priced houses, specifically those falling within the 20th, 30th, and 40th quantiles of the house price distribution figure 6 illustrates higher priced homes that are more impacted by airbnb activities, while fig. 7 highlights houses with lower prices that are less affected. visually, a noticeable difference becomes evident between these two maps for the cities virginia beach, williamsburg, and chesapeake. many of the houses experiencing a higher price impact are situated in these three cities, whereas only a few of those with less impact at lower prices are located there. this observation aligns with our earlier explanation that more incentives for short term rental listings in tourist areas, leading to increased demand for properties, will result in higher prices for houses. this is especially true for virginia beach and williamsburg, recognized as top tourist destinations in virginia according to various tourist guide websites.footnote 7 to further analyze how airbnb activity impacts house prices, we refined our approach by replacing zip code fixed effects with census block fixed effects, capturing neighborhood characteristics more granularly. census blocks cover smaller areas, allowing for better control of neighborhood level heterogeneity in housing prices. results, shown in table 12, remain similar across all tables, suggesting that neighborhood effects are not the primary driver of price heterogeneity. we then conducted separate regression analyses for entire place and private room airbnb listings, with approximately 63% of listings as entire places, 35% as private rooms, and 2% as hotels/shared accommodations. listing counts were recalculated for each type, following our three measurement criteria. table 13 displays quantile regression results, indicating that entire place listings yield statistically significant effects across most quantiles, with slightly stronger effects compared to our main findings. for example, the 70th and 80th quantiles for the second measure of airbnb activity (table 13, second row, entire place listing) show coefficients of 0.0069 and 0.0067, respectively, while the 20th, 30th, and 40th quantiles have coefficients of 0.0019, 0.0020, and 0.0029. this pattern suggests that entire place listings significantly drive up prices, appealing to a broad market including families, groups, and tourists who seek privacy and are willing to pay a premium. consistent demand for these homes, across neighborhood price levels, amplifies airbnb’s impact on prices across different ranges. conversely, private room listings show largely insignificant results across quantiles, reflecting a more limited appeal. private rooms mainly attract budget conscious or solo travelers, contributing less to local property demand and value. thus, while private room listings contribute to airbnb activity, they lack the substantial influence seen with entire place listings, reinforcing heterogeneity in price effects across housing segments. entire place listings uniquely elevate prices in high demand, high priced areas, intensifying airbnb’s impact on high range properties. in previous literature, critics of short term rentals (strs) have argued that the concentration of strs in a neighborhood can cause negative externalities, such as higher levels of noise, littering, less space for parking, and safety concerns in the neighborhood (gant 2016) along with increasing inequality (schor 2017), financial hardship (daniels and grinstein weiss 2019), and lowering city livability (barrios et al. 2023; erhardt et al. 2019). however, some studies (e.g., sheppard and udell 2016) discuss the positive side of strs, such as generating a new income stream for homeowners, decreasing the cost of owning a home, and increasing property values. this study adds to the growing literature by estimating the effect of strs (airbnb) on housing values in hampton roads, virginia. furthermore, we explored the time and distance effects related to airbnb activity and examined how airbnb rental activity impacts house prices across the price distribution of home sales. we used a hedonic regression model to investigate the effects of airbnb activities on neighboring house prices. for this purpose, we used three different measures of airbnb activity to examine whether airbnb’s impact on house prices is robust when different measures of airbnb are used. the main results reveal that airbnb activity has a positive impact on housing prices. this result is consistent across all three measures. the average magnitude of the impact is approximately 0.27% for the first measure (number of active airbnb listings), 0.24% for the second measure (number of airbnb listings with a positive occupancy rate), and 0.13% for the third measure (total number of reservations). the third measure differs from the first two because it measures the intensity of the guest traffic. we expected the number of reservations to be a better measure to capture any negative externalities associated with str activity. although the average magnitude of the impact for this measure is less than the effects of our first two measures, it is still positive, which shows that the positive impact associated with str activity dominates its negative externalities and that the net effect on house prices is positive. furthermore, our distance effect model reveals that the localized impact diminishes over geographic space (i.e., distances greater than 500 m). for instance, the results for the first measure show that an additional airbnb listing at 300 m from the property has a 0.36% impact on house prices, while one more airbnb listing at a 300 to 500 m distance from the property increases its sale price by 0.08%. finally, one additional airbnb listing at a 500 to 1000 m distance from the property decreases the house sale price by 0.07%. moreover, as expected, the period closer to the sale has a stronger effect. for all three measures, we find significant and higher positive coefficients for airbnb listings in the months of s to s 3 (phase 1) compared to airbnb listings in the months of s 4 to s 7 (phase 2) and s 8 to s 11 (phase 3). finally, our study employed unconditional quantile regression to assess the impact of airbnb activity on house prices across its distribution. the analysis revealed a noticeable upward trend in the impact of short term rental activity from the 20th to the 80th quantile. the most pronounced effects were observed for the 70th and 80th quantiles, indicating a stronger influence on higher priced properties. in contrast, houses within the middle price range experienced an average impact, while lower priced houses, specifically those in the 20th, 30th, and 40th quantiles, exhibited a comparatively lower impact from short term rental activity. this heterogeneity may reflect increased demand for accommodations in high priced, tourism focused areas, where entire place listings have a stronger effect on house prices than private room listings. these findings highlight how airbnb activity disproportionately affects high 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tailed distributions. j am stat assoc 107(500):1453 1464 article google scholar zervas g, proserpio d, byers jw (2017) the rise of the sharing economy: estimating the impact of airbnb on the hotel industry. j mark res 54(5):687 705 article google scholar download references j. whitney bunting college of business and technology, georgia college & state university, milledgeville, ga, usa navid safari department of finance, strome college of business, old dominion university, norfolk, va, usa lei zhang department of economics, colorado state university, fort collins, co, usa timothy m. komarek you can also search for this author in pubmed google scholar you can also search for this author in pubmed google scholar you can also search for this author in pubmed google scholar correspondence to lei zhang. springer nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. see tables 7, 8, 9, 10, 11, 12, and 13. open access this article is 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the problem is, the following things happened as a result:    existing short term rental properties were grandfathered, which artificially has made those properties more valuable.    a lot of properties that might have previously been purchased and renovated by investors sit idle because the investors can't turn them into short term rentals.    we have a lot of cottages / really small properties here, that are good short term rentals but not great regular rentals. so basically a lot of garbage rentals came available that did nothing to actually help our rental market.    the hotels knew they now had a more captive market than previously. so while i appreciate the sentiments of those who wanted to end short terms rentals in the name of affordable housing, at least in our local market it has not been a good thing. so, while airbnb is both good and bad, i prefer to value native peoples ability to live in their places, rather than helping the rental market, that was just already stretched when airbnb arrived.   one page contract   ban loans for private home ownership (or socialize them but you would have to do that in a a very particular manner)   no property taxes for up to two homes maybe an additional required indicator on that page of “is purchasing this home a good financial decision for the purchaser given their income” on a scale from 0 10. just a month ago i saw a coworker get screwed financially on a home purchase. and now with a recession knocking on the door, oof. i’m curious to better understand your co workers situation as well   was it a case if overextending themselves or caveat emptor on some part of the transaction? this is a good thing. because it means that the properties can be occupied by people actually living there. we have a lot of cottages / really small properties here, that are good short term rentals but not great regular rentals. so basically a lot of garbage rentals came available that did nothing to actually help our rental market. cottages are great rentals. they may not fit your needs, but in large cities, there will definitely be thousands or hundreds of people for who cottages are the perfect fit. most of what you describe is actually good for the rental market for the people actually living and working in the community. the hotels knew they now had a more captive market than previously. hotels cannot "capture" the market. they are dependent on tourists coming in to the town, and that means that other parts of the town must provide a reason to visit. right now, hotels are bleeding money. the lesson there is not to grandfather privileges if you want to actually fix something. this happens all of the time. they frequently lobby cities to push out other short term rental options that they have difficulty competing with, which leads to regulatory capture. if you restricted car rental services from buying cars, you might very well cause the collapse of the automobile industry. in contrast, killing airbnb does not negatively effect the housing market, because people still need places to live. airbnb generally increases the cost of housing in every housing market in which it operates because it reduces the number of housing units available. (example: once la and santa monica cracked down on airbnb and began regulating short term pseudo hotel stays, rents plateaued and began dropping, benefiting thousands of people in the la area. the only people harmed were the investors who bought multiple residential properties and illegally converted them to unregistered hotels.) cities should allow enough housing to meet demand for both short term and long term rentals. all the builders are busy. getting construction help around here is a crazy process these days (referring to the year bcp / before covid pandemonium)   builders that have the ability to build are generally focused on building more expensive houses to get more money (not average homes that were built long ago). the ones that have the money (from whatever sources) to build the minimum code skinny homes that do well for maximizing cost for arbnbs seem to be gobbling up any and all possible property that fits in the zoning places that these can squeeze into   limiting the market for others who want to build their own (who already live here) the part of town i currently live in, the neighbor groups are nimby nimby   a recent meeting i sat in where a local developer was exploring putting in a dozen townhomes / condos into a larger development with single family homes   everyone there just wanting to complain about the effects of other development in the area. the price of land is going up, and the cost to develop the land. building affordable is not as easy as it was some time ago. some of council is telling citizens to downzone property en mass in order to prevent more affordable options from popping up in their neighborhoods. i heard our last council meeting they attempted a county wide zone change. it's become clear to me that some of these council folks really are 'our neighborhood character, the children!' type people.. however it seems that many of them are spouting similar types of scare mongering things while really just propping up the value of all the marriot properties in the city (by trying to limit the airbnb and similar). it's been an interesting show to watch. i don't think supply and demand is going to turn all these, now mostly empty, hotel properties back into affordable housing for the locals. they will just sit there and maybe be traded as distressed properties and be rebranded as some other brand new basic like the self storage places. at least that's how i see it around here in a nutshell   there are many other interesting dynamics going on. quite the complex play to watch actually, with characters from all over vying to push norms wherever they can get away with it   while the average families try to figure out where they can afford to live. the things the surrounding counties are doing to prevent the people from here moving there is also another interesting set of things to watch with wonder. one consequence of uber is more cars on the road. if only one consequence of airbnb could be yimby... how does this work out? i know so many people who just take cabs now and haven't purchased a car in a long time because of how easy uber has made cabs and ride sharing. surely this means a lesser number of cars on the road? increasing cars on the road + pollution. although you could argue there are technically less cars, it feels like there are more. 1) build a ton of housing to catch up with demand 2) shrink your local economy to decrease demand if a lawmaker isn’t working on one of these two options, they do not actually support a platform of low housing prices. airbnb is only a small slice of housing demand. it’s an ugly slice, which makes the company a convenient punching bag for politicians who want to act like they are trying to make housing affordable. but the economy in high demand cities will keep growing regardless of whether you kill airbnb. i used to be very active on a forum for urban planners. they made a very strong case that a few decades back and in living memory of some older members, small coastal towns in the us were affordable and pleasant and similar to other small towns. then the practice of owning vacation homes drove up prices out of reach of locals and tourism brought other problems. and this was before airbnb was a thing and magnified such issues. you need to build more housing and all that. but you also need to say "housing is primarily for the people who actually live here and the wants and desires of wealthy tourists absolutely doesn't trump that." or basically the entire world lives in a shithole so a tiny fraction of the population can do whatever the fuck and indulge their every whim cuz money. this is not a good thing. not even for the rich people getting away with it while people say "eat the rich." there is a long history of the lower classes violently and murderously removing the elites when they do that. i'd like to not "live in interesting times" that include violent revolution. covid19 is enough fun for me, thanks. i'm a boring fuddy duddy like that. party pooper. don't drink. etc historically, we used hotels to visit places we spent limited time in. (or you can camp, visit friends, etc.) units converted to airbnbs were not designed for short term stays or taxed on that basis, and generally airbnb only exists because it leveraged regulatory arbitrage quickly enough to grow to the point it could lobby for changes to the law. but after multiple studies in multiple cities confirmed that only a handful of investors benefited from loose unregulated short term rentals, the pendulum began swinging back towards regulating them like the hotels they are. there have been articles about this. it sticks in my mind because i used to be in insurance. residential buildings were not intended to be used this way and all the policy and infrastructure points to "no, really, you weren't supposed to use this as a commercial vehicle. that's not what all of society agreed to for this piece of real property." for landlord insurance i'd pay about £300/year in scotland, and €60/year in finland. i'd be surprised if there were short term policies, as distinct from long term tenant policies, that we significantly more expensive than that. housing is a basic human right. but this is denied to millions of americans, simply because those that came before them, were able to secure affordable housing for themselves, while locking out others from attaining affordable housing. how about close proximity to family and relatives? sure, you can move to middle of nowhere texas, and buy land for cheap, but how are you going to pay for that cheaper mortgage, if there are no jobs around? maybe you can get a telecommuting job? however, at some point, their votes interfered with the human rights of others. at what point does the government and society step in, and say, enough is enough? meanwhile airbnb creates a level of convenience and affordability that makes travel accessible to the masses. i'm sure the software engineers down voting wouldn't be significantly affected if airbnb was forced out of operation. just another version of nimbyism. but, if affordability really is your top goal, you'll do even better if you go with a hotel that offers kitchenette suites. in my experience, they typically offer a lower nightly fee than having an airbnb to yourself (especially if you are staying at least a week and don't require someone else to come make your bed for you ever day), and still let you save on meal costs. (airbnbing a room out of someone's house is even less expensive, but i don't think that style of airbnb rental is really the subject of much controversy.) i've also seen articles about how tourism is destroying cities, destroying the environment, etc. meanwhile, an upside to the current pandemic is a massive drop in pollution so extreme it's visible on satellite imagery. i am reluctant to agree that optimizing for the right of "ordinary people" to pursue jet set lifestyles is really in the best interest of humanity. i'm happy to let people travel. but "let's ruin the planet for touristy shit and claim it to be some egalitarian ideal to open that path to less rich people so they can join in on that" is not really a value that strikes me as belonging in the wise and idealistic column. mayhap we can find something better if we put a little effort into it. absolutely not something i'm advocating. i talk a lot about housing issues. i think it can be solved without making landlords into the victims here. that's a long conversation i don't really want to have at the moment. this comment might cast a little light on it: https://news.ycombinator.com/item?id=22589980 in a "fair" world, property would appreciate at the inflation rate. no more, no less. it's also not fair to help people make money on property at the expense of the poor and the younger generations. so where does that leave us? also, i had no car. hotels always had some eateries and the like in walking distance. if an airbnb is basically in the middle of fuck nowhere with no amenities nearby, it does you no good if you have no car and very limited funds. i stayed a lot in two hotels in fresno. one had a chipotle, mcdonald's, two restaurants and a 7 11 right on top of it and the bus stop was also practically right on top of it. there was more shopping about a 15 or 20 minute walk away. the other had a taco bell practically up against it and more stuff a few minutes walk away even though it was in the worst part of town with the fewest amenities. this also meant it cost less than the other one. clearly a greater good than, say, having a place to live, no? you have a point with your first paragraph   it would have been a good place to stop. until the bigger issue is addressed, optimizing for a hypothetical situation is premature. hypothetical? we've seen travel numbers increase significantly over the last decade, and airbnb now claims a large chunk of that. i think it's important point to remind people why airbnb exists    it's a great service with positive benefits. i have stayed in far more airbnb's than hotels over the last few years, saving money and typically getting a better overall experience. > clearly a greater good than, say, having a place to live, no? this goes back to the first point. let people build more units. if that doesn't fix the problem, then start evaluating more extreme measures that limit the amount of people that are able to travel to an area at a given time (in the name of making sure people have a place to live.) to be absolutely clear: you have identified a greater good if and only if your proposed solution for making housing more affordable is implemented successfully. the post would read almost exactly the same if it addressed your points in the opposite order. saying it would 'answer' the question as if you're the total arbiter of this is just not true. they're not confused about what you mean, they're disagreeing. and it's really not a step one, step two situation anyway. both parts are important at the same time, in different ways. and your example is incorrect. you can buy or lease a car just as you can with a house/apartment. but there does seem to be some basis for this eg [1] [2]. and i can believe it. i've long since held the opinion that airbnb is a cancer on society. while uber allows you to get value from a vehicle you may already own (at the cost of wear and tear, mind you) and there are many, particularly those in the us, who seem to hold this view that "it's my home, i can do what i want with it", the reality is that neighbours of that airbnb bear the externalities and the cost of your listing and it's not easy for most people to up and move. another way of putting this is that it is a tragedy of the commons as airbnb "hosts" extract value from a community that bears the cost with little choice in the matter. this lazy (and typically selfish) twisted version of "free market" thinking quickly erodes when, for example, i propose using my house that's next to yours as a tannery or a toxic waste storage site. my only complaint about jurisdictions trying to crack down on this is that their efforts have been too slow and anaemic. nyc is plagued with what are essentially illegal hotels. there are a reason that hotels exist in the current form and place. it's for the safety of guests (eg compliance with fire codes) and the benefit of residents (ie zoning). [1]: https://www.independent.ie/world news/coronavirus/homes for ... [2]: https://www.balls.ie/the rewind news/something interesting h... there’s a reason my building is currently half full, and it isn’t because everyone is out of town. if there’s any silver lining to this whole terrible pandemic maybe all the crooked rent to airbnb people will go bankrupt. comparing the number of cars in nyc/dc/etc garages vs on street parking people are still mostly using company provided / reimbursed the garages down town. so, while some people in some areas may spend time circling, across all trips average circling time is minimal. what toxic waste, exactly, do airbnb listing dump on their neighbours? professional airbnb investors buy up whole properties, renovate them, run them as a hotel with regular cleaning and services provided. their shot term residents use the same amout of public transit, water and utilities services as long term renters next door. the only real inconvenience is that it's driving property prices up   but that's not an externality. that's just a reality of a market where some type of customers agree to pay a higher price for something, and it's price goes up for everybody. i don't see any reason why market should be artificially tilted towards long term renters as opposed to short term. i absolutely love using airbnb and prefer it over hotels in nearly every place i visit, but i won't pretend it's somehow good for the neighborhood or neighbors. in amsterdam we saw this a lot, with "party tourists" staying in airbnbs, getting home at 4 am and then shouting and making noise constantly, effectively ruining the lives of the next door neighbors. this is a huge problem, especially since amsterdam has many old houses that do not block much noise. imagine waking up three times per week at 4am because your neighbors' airbnb guests are shouting after coming home drunk/drugged from a party again. i bet you would not be very happy either. unlike in the us, though, thankfully the government has cracked down very hard on airbnbs. it is now illegal to rent out your house/apartment on airbnb for more than 30 days a year, and people renting our their apartments on airbnb must pay high tourism taxes to pay for externalities. it is also only allowed to rent out your primary residence, you cannot rent out a second apartment or house. people trying to circumvent these laws are cracked down on hard and have to pay extremely high fines (more than €10k). home owner's associations and rental contracts now also typically have clauses that explicitly forbid airbnbs. still, many people want to introduce more rules or even ban airbnb entirely. not in nyc. here they violate both the law and leases/condo rules to airbnb units they bought or rented under false pretenses. airbnb knows this and doesn’t care. it’s not about the free market, it’s just another swindle. unlicensed hotels, mind you. >what toxic waste, exactly, do airbnb listing dump on their neighbours? there was a massive issue in my town where students would rent airbnbs and party, keeping neighbors up when they had to go to work the next day. police were having to attend to more and more incidents. and yet a free market has never existed. market forces made housing expensive at a faster pace than regulatory changes. there are simply many more people now in their 25s 35s than there were 40 years ago, more dual income households, and more costs (i.e., college) associated with landing the high paying jobs than there were 40 years ago when you could get a 6 figure job with full health benefits and a pension at a factory without being able to read or write. absolutely not. a fundamental requirement for a free market is to avoid barriers of entry to competition as much as possible. in the real estate market there are way too many barriers of entry to be considered even remotely free:   zoning laws   construction permits   licenses (realtor, construction, architects...)   all kinds of taxes so no, real estate has never been a free market. the blame for today's problems lies with governments' intervention and regulation. edit: formatting out of curiosity, what do you think of net neutrality? i'm down for a good "free market" critique, but this veers hyperbolically into nimbyism. everyone agrees that a tannery or toxic waste dump is inappropriate near residences. but having people stay, eat, shower, and sleep in a home is literally what houses are designed for. if you don't own the home, you don't get to choose who stays there (obviously if the renters are breaking the law, that's a problem, and you can report them to the police). i'm also super sympathetic to the need for (affordable) housing in most cities, but banning airbnb has a relatively negligible effect on that ginormous problem. i think you'll find that's trivially untrue. it's fair to say the vast majority of people agree or even almost everyone. but to find one counterexample? i bet i could do that. my point here is that the argument of "it's my home i can do what i want" is fundamentally flawed and all it takes is a trivial extreme case to debunk it. so when you move on to what's allowed and what isn't, what's good for the community, what isn't and so on, which is essentially what zoning is. > but having people stay, eat, shower, and sleep in a home is literally what houses are designed for so i'm supportive of people hosting other people in their homes, which, incidentally, nyc allows (as one example). why? because then the host is invested in that community and can police bad behaviour. when hosts aren't present, they have no such incentive. in years passed, having long and short term boarders was commonplace. many cities in the post wwii era however essentially dismantled boarding houses and sros (which were once where a significant number of people lived) and in some cases outlawed multi family residences. there are extreme examples of what can happen in an airbnb [1] [2] [3]. there are countless stories of guests being stranded when hosts canceled at the last minute and scams such as bait and switch. [1]: https://10daily.com.au/shows/10 news first/a190520wykmx/poli... [2]: https://www.abc.net.au/news/2019 11 03/airbnb bans party hou... [3]: https://www.dailymail.co.uk/news/article 7818881/airbnb host... you've positioned your overall argument as championing the community against an invading force (airbnb), but the claims so far seem to serve a very narrow constituency (and who is the constituency as you see it?). i'm eager to hear substantive assertions that favor the wider community, rather than a narrow constituency, regarding airbnb. i'm open to the notion that airbnb is bad for the community, but the arguments haven't been convincing so far (note that scams and scammers reach far and wide). your case is different. zoning allows agricultural where you are. really no one has any cause to complain about that. as an aside, people do complain about this sort of thing. for example, people will move to where an airport is proposed to be (eg badgerys creek, sydney) or where an existing airport is (eg lax) and then complain about the potential or actual noise to get restrictions on flights (or on building it at all). i don't have any time for these shenanigans. that's here in merida, at least. i wish the rental market would crumble. it's absurdly difficult for a normal person to rent a decent dwelling. the hostel i rent at is full of people in a similar situation. the more conventional rentals get replaced with airbnb's that allow monthly stays, the better. the supposed legal protections of conventional renting don't exist, and airbnb is actually a better enforcer of agreements than my government. security deposits and upfront rent address the problem of deadbeat tenants who may destroy the property or simply not pay the rent. it’s skin in the game to show you’re responsible. there are some interesting models around the world for alleviating this specific burden that don't involve corporations being everyone's landlord. my favorite is one i learned about relatively recently: rental deposit guarantees[1] in switzerland. the renter pays a small premium to e.g. swiss post, which will issue a certificate guaranteeing the amount of the deposit. the landlord accepts that certificate in lieu of cash. 1. https://www.post.ch/en/locations/rental deposit guarantee i'm sorry to hear that your government is not protecting tenants rights. in other parts of the world, you do have more rights as a long term tenant than as an airbnb user. and airbnb's enforcement of their own policies is a joke, considering all the scams and fraud people get away with on the platform. in other words, if airbnb is somehow preferable to renting... you've got bigger problems to worry about. it's not ideal, but the barrier to entry is way lower for a premium that is not really high imo. out of curiosity, where are you from? my rental (traditional lease) cost is ~10x yours and i am considered to have a screaming good deal. >i would assume this discussion is centered on coastal american cities did you click the link of this submission? it's about dublin, ireland. that said you're right i have never checked out airbnb prices in the coastal us and i'd assume they are much more expensive there that doesn't mean there aren't airbnbs that would otherwise be longer term rentals. of course there are, the logic is just bad. this also doesn't necessarily mean rentals used for airbnbs are inherently "bad," either. it's complicated. now it seems there is evidence. https://www.daft.ie/blog/the covid 19 crisis is already affe... this is probably not where the founders really meant for it to go. a large part of the growth in tourism over the last 15 years can be attributed to cheap flights and cheap housing, via airbnb and the likes (i.e hotels have not added the number of rooms needed to support this tourism boom). given the tepid economic growth in the euro zone over that period, i doubt they want to kill the golden goose. [1] https://tcdata360.worldbank.org/indicators/tnt.tot.contrib.g... and if you're worried about losing housing to rentals, now you've got some disused hotels you can turn into apartments or condos. it's regulated because of suffrage arbitrage    most of the local population doesn't care what the government does to tourists and the tourists can't vote in local elections. but that's just taxation without representation. it tends to be inefficient, because it suppresses the natural amount of tourism by over taxing it to the detriment of the local economy. it also hurts everyone when everyone does it because then you're the victim of it on the day when you're the tourist and it's you who has no vote in unreasonable lodging restrictions or taxes. and cities don't need to regulate the amount of housing in order to build infrastructure for it, all they have to do is observe it. if you build more buildings they need more sewers, but you shouldn't need their permission to build more buildings, they should just build more sewers with the property tax you'll be paying on the new buildings. > people who save bucks on usually one one thing are usually not just frugal in that dimension. that sounds dangerously close to an argument that poor people are undesirable and should be excluded. and it isn't even accurate    poor people have to eat too, and better that you get their business than somebody else. plus, rich people will take a good deal on a room (see also: how they got rich), doesn't mean they don't have money to spend when they get there. if uncontrolled, tourism takes over those places, eventually, there is little left for tourists to come to, what made the place interesting to begin with. unregulated conversion from housing capacity to other uses destroys the social core of neighborhoods, if there are no hard enforced laws against it. tourism for a few bucks cheaper in not human right, right to housing is, disproportionally more relevant to the economically most vulnerable people of each neighborhood. my family travelled france on a budget in the early 90s just coming out of gdr. doing housesitting, camping or hotels in the outskirts, it was perfectly fine travelling into paris for 45 minutes to do sightseeings and museums. doing this on a daily basis, because you were pushed out of your old neighborhood, because of indirectly rising rents or direct airbnb repurposing, where your job and friends are still, is a whole nother story. https://en.wikipedia.org/wiki/right_to_housing then why would you expect it to have any major effect on housing availability there? > tourism for itself only works for purpose built resorts and specialized, not so urban destinations disagree. look at new york city. it's full of tourists who come there for the city itself. and it has hardly destroyed the city. > unregulated conversion from housing capacity to other uses destroys the social core of neighborhoods, if there are no hard enforced laws against it. this is only true if building new housing is prohibited, because otherwise conversion isn't necessary. if tourists want to come, converting existing housing only makes sense if it's unoccupied, otherwise it's more profitable to carry on collecting rents from existing housing and build new housing for tourists so you can get their money too. (there may be cases where it makes more sense to build new housing for long term residents while converting existing stock for tourists depending on the nature of the existing stock, but that still isn't a reduction of housing stock for existing residents.) what deprives people of housing and makes it cost more is refusing to build more in respond to higher demand. quite a bold claim, care to back that up? do you think an undocumented immigrant would get a better deal in sf? what kind of comparison is that and for what so poor renters who might not go out and vote for said issues when push comes to shove. one, hotels charge way too much money. when a hotel room costs $300 to $500 per night, then this will quickly burn your travel budget. two, airbnb hosts are often, rather dishonest with their listings. but, airbnb is the only way to rent someone’s house in a distant location, where your family and extended family can stay together to get away. it’s impossible to do this with a hotel, and very expensive too. initially, some people got enterprising, and rented out their homes to help pay their mortgages. and they charged a modest fee for it. for a while, this seemed ok, as you were helping out someone to pay off their expensive mortgage. like, yeah! stick it to the rich bankers, and all the jackasses that contribute to expensive housing. but then, soon others, turned it into a hotel operation, where investors would team up to buy houses and apartments, only to rent it out on airbnb. and when this happened, everyone else started doing it, and so the initial airbnb business model was no longer socially or morally acceptable. you and i, as an individual, cannot compete against a team of 5 investors that buys the property outright. our individual goal, is to put down roots, build a family, and contribute to the community so our children can have a stable place to live. their goal, is to make a profit, and later flip the house for an even larger profit. for better or worse, maybe this coronavirus will destroy the airbnb business model. its initial thesis, is no longer socially acceptable. however, building new supply is very much demand driven. and airbnb pushed up demand, which lead to more units built. (probably some hotels were not built, etc.) this "parasite" thinking is just impotent rage. the real problem is the lack of housing supply. city dwellers are rather resistant to change. development is noisy, draws more traffic, casts a shadow, etc. yet most of them are also renters. it's the classic participation asymmetry. the vocal minority trumps the silent majority. i guess one group of people are suggesting that we adjust the demand to the local people who live and work in the city rather than allowing the demand of tourists to take the supply. in some areas it's also simply not possible to change the supply as all the land who could be built on, is already built. i for one would love to see a re prioritization for the local people in favor of tourists, and we're slowly seeing that shift happening now. but directly prohibiting short term rentals just because they take up units is the same short sighted "solution" as prohibiting high density mixed use development. cities that are growing/booming/trending are attracting people all over the world nowadays. for work, for leisure, for simply living there. i'm not saying just start building high rises like there's no tomorrow. (actually i think simple quotas with lottery systems would work best.) control the influx, not just one symptom. > land who could be built on, is already built. could you describe such an actual example? i can't really think of any place on earth where we can't easily increase population density by building up. this is why comparing long term rentals and airbnb makes little sense: yes, half of the flats for rent at any given moment are for short term. but they're always the same ones, over and over again. they could increase the long term offer only for a few days before disappearing for good. paywall, but even just the headline and first paragraph tell you what you need to know. and yeah, i don't see how this doesn't completely decimate their business model. the likely coming worldwide recession will see far less spending on travel, not even counting residual behavioral changes re: covid as you mention. update: non paywalled article: https://finance.yahoo.com/news/airbnb mulls cash infusion in... i doubt this will change anything longterm unless it becomes proof for policy makers to ban airbnb. what grounds did the landlord use to evict you? how long between you guys leaving and the airbnb business starting. did you receive any and all relocation payments required by law, and has the landlord continued to file the required annual paperwork with sfrb? if the eviction reason was either 'owner move in' or 'relative move in' then, if the landlord subsequently decided to rent out the unit again with the next few years, they are required by law to first offer the unit to you at the original rent. if you decline, they can rent to someone else, but again only at the original rent (plus the city mandated max annual increase %). (and, no, the landlord can't get around this by airbnb'ing the other unit, and moving in to your unit.) i'm less familiar with ellis act evictions, but my understanding is that these also prevent the landlord from re renting the property for a significant period of time. i have an airbnb off my garage. it's a granny flat, a bed, a small bathroom and a kitchenette. a sink, but just a hot plate a fridge and no oven. it's not somewhere you'd want to live for a long time, in my opinion. but it's a great place to crash for a few days, have a cold breakfast, some oatmeal, reheat left overs, etc. there's people like me who it helps live in sf and offset property taxes, price of the house, etc. bad actors/bad markets/etc certainly do exist, but like all things it's complicated. making money off people on vacations is worse than providing stable housing for people in the city. https://www.forbes.com/sites/forbesfinancecouncil/2020/03/12... if we have to reboot the economy, let's get rid of the vilest anarcho capitalist and environmental destructive cruft we have allowed to fester over the years. hotels cost so much for a place that doesn’t even have a hot plate. it's not sufficient to show that this makes more money for the owner. it doesn't exist in a vacuum. if it costs the rest of the world too much, no, it isn't the highest and best use by any stretch of the imagination. edit: see for example this hospital decided to just house a few people to lower its expenses: https://www.wbez.org/shows/wbez news/homeless patients get n... please note that i am not actually for "housing first models" or free housing. i'm for market based housing that makes sense. it's just a nutshell description of the problem space to note that just giving people housing can be cheaper than leaving them to fend for themselves. "the original study that generated so much headlines in the press said this was true for _temporarily_ homeless    it would, in fact, be exorbitantly expensive to house _all_ homeless people." nonsense. over 80% of homeless people on a given night are temporarily homeless. there's less than a hundred thousand chronic homeless in the entire country. $100 a week would be enough funding for a hotel, but let's drill in the point by doing the math for $200 a week. 90 thousand homeless * $200 * 52 / 320 million people. final cost: $3 per capita per year to house every single chronically homeless person. and that's not even getting into the strawman they made about forcing landlords to do anything. how do you know that the most productive user a resource is something other than the highest bidder? on what basis can you make that determination? prices are universal. note that in some cases that won't be the optimal way to distribute the food. for example perhaps the starving person is actively hurting society while the person with excess food wanted to use the food to perform research that promotes greater food production in the future. however, taken as a whole, allocating at least some food based on need is going to perform better than allocating all food based on price. if you think property is too expensive in your city, consider building more of it instead of imposing arbitrary restrictions on demand. libertarian bullshit and 'the market' have done enough damage. see also: commercial healthcare. you probably don't want to place housing in an industrial area. there are environmental implications that can affect public health. likewise, you probably don't want to place a ten story apartment block in a neighborhood filled with single family housing since it is going to interfere with other property owners enjoyment of their own land. yet there is more than zoning to consider. building codes raise the cost of construction, and ultimately cost of housing. historically, parts of cities have been lost to flames because of how building were constructed (among other reasons). now i am not saying that all of the regulations are good. on the contrary, i believe that they are too restrictive to meet housing needs. on the other hand, you will always have someone calling shenanigans based upon the philosophy behind those regulations and how the community views that philosophy. https://www.nytimes.com/interactive/2016/05/19/upshot/forty ...

primary navigation book contents navigation title page acknowledgement of country foreword contributors 1. sharing economy, collaborative consumption, peer to peer accommodation or trading of space? 2. the evolution of airbnb’s business model 3. the evolution of airbnb’s competitive landscape 4. the evolution of airbnb regulations 5. hosting and co hosting on airbnb   before, during and after covid 19 6. resident satisfaction with the growth of airbnb in ljubljana   before, during and after covid 19 7. airbnb in china   before, during and after covid 19 8. airbnb’s offerings beyond space   before, during and after covid 19 9. airbnb catering to guests with disabilities   before, during and after covid 19 10. airbnb catering to the multi family travel market   before, during and after covid 19 11. airbnb and events   before, during and after covid 19 12. activism, lobbying and corporate social responsibility by airbnb   before, during and after covid 19 13. airbnb’s space donation initiatives   before, during and after covid 19 dorine von briel, department of tourism, uq business school, the university of queensland, australia sara dolnicar, department of tourism, uq business school, the university of queensland, australia please cite as: von briel, d. and dolnicar, s. (2021) the evolution of airbnb regulations, in s. dolnicar (ed.) airbnb before, during and after covid 19, university of queensland doi: https://doi.org/10.6084/m9.figshare.14195972 the need to regulate short term accommodation when it was small in scale, the trading of tourist accommodation among ordinary people created no difficulties for local residents, tourism destinations, or traditional tourist accommodation providers. in fact, the trading of space among ordinary people existed long before the emergence of online peer to peer accommodation platforms such as airbnb. the swiss and dutch teachers’ associations, for example, started their home swapping program for members in 1950 (gallagher, 2017). the city council of varna in bulgaria invited residents to open rooms in their homes to tourists in 1896 (varna municipality, 1896). airbnb’s convenient online peer to peer accommodation trading platform suddenly enabled millions of people around the world to sell access to space to strangers. high demand for peer to peer accommodation, initially perceived by tourists as cheaper and more authentic than traditional tourist accommodation, led to substantial structural changes at destinations around the world, especially at popular tourist destinations. landlords in the long term rental market saw the opportunity to earn more money and transitioned to the short term accommodation market. residential areas with a traditionally low tourist presence experienced an influx of visitors, affecting the use of space in neighbourhoods originally designed for use by locals only. established commercial accommodation providers became concerned about unfair competition reducing their market share (von briel & dolnicar, 2021). these changes forced regulators to assess whether existing legislation and regulation offered residents and the local tourism industry adequate protection from negative externalities, while still allowing additional economic benefits to be harvested. regulating peer to peer accommodation proved challenging as most platforms facilitating the trading of private space for short term accommodation, such as airbnb, are global organisations operating in local legislative frameworks (brail, 2017). in many cities, the activities of peer to peer accommodation platforms facilitators were breaching existing regulations, in others, their activities were unregulated. whichever the starting point, the number of listings increased dramatically at locations with high tourist demand, forcing regulators into action. regulatory approaches varied substantially from location to location because each destination faced a unique tourism demand pattern and a different set of negative side effects. regulators had to prioritise their goals, which typically included reducing transaction costs, increasing efficiency in the market, promoting equal distribution, and ensuring public safety, as well as health and consumer protection (lobel, 2018). the emergence of peer to peer accommodation networks had positive and negative effects on local communities, and local residents generally acknowledge this fact (jordan & moore, 2017). on the positive side, peer to peer accommodation networks offer people without permanent (or sufficient) employment the possibility to earn money or to supplement their income by renting out spare space. beyond hosting itself, a large number of other micro entrepreneurship opportunities have emerged (sigala & dolnicar 2018; fairley et al., 2021a), including intermediaries between hosts and guests, and services assisting hosts such as cleaning, gardening, pool maintenance, and laundry. the ability to make spaces accessible quickly in a specific location has also proved invaluable in communities facing unexpected disasters, such as earthquakes. peer to peer accommodation platform facilitators are able to offer critically important assistance by accommodating displaced residents and tourists (hajibaba & dolnicar, 2017). despite all these benefits, the exponential growth of short term listings at popular tourist destinations created some significant challenges (dredge & gyimóthy, 2015; dolnicar, 2019; lee, 2016; oskam & boswijk, 2016; hajibaba & dolnicar, 2018). tourists using areas designed to be residential can significantly burden local infrastructure. parking spaces, for example, are suddenly used by residents and tourists. the town planning process had never accounted for this additional demand, causing shortages. the presence of tourists can also change how space is being used, completely transforming the character of the neighbourhood (zale, 2018; gurran et al., 2020) and negatively affecting the quality of life of residents. other aspects causing frustration among residents include additional noise   such as the much referenced sound of roller suitcases   and safety concerns due to the presence of non locals with no place attachment or sense of obligation to behave appropriately. people living in apartment blocks experience anxiety because apartment keys for high security residential buildings can change hands daily (williams, 2016). the lucrative business of   often unregulated and unlicensed   short term accommodation (lee, 2016; gurran & phibbs, 2017) can also affect local real estate markets. houses and apartments rented out to long term tenants are repurposed to become short term rentals (ferreri & sanyal, 2018; zale, 2018) which reduces housing availability and affordability for locals (gurran & phibbs, 2017; lambea llop, 2017; ferreri & sanyal 2018; zale, 2018; gyodi, 2019), and creates gentrification (wachsmuth & weisler, 2018). in new york, for example, property value rose by 6 11% every time the number of airbnb listings doubled (sheppard & udell, 2016), and rental prices increased, especially in wealthier areas (wachsmuth & weisler, 2018). across the entire united states, an increase of airbnb listings of 1% led to a price rise of 0.018% in rent and 0.026% in house sales (barron et al., 2018). unhosted rentals   spaces available for short term rental without the host being present   are common, particularly in popular tourist destinations where second homes and apartments are listed when unused (adamiak, 2018). unhosted rentals have the most detrimental effect in popular cities with a deficient housing stock. in london, for example, 52% of listings were unhosted in 2016, increasing to 56% in 2020. in 2016, nearly 42% of hosts listed multiple properties. this proportion increased to 49% in 2020 (ferreri & sanyal, 2018; inside airbnb, 2020). because landlords believe they can earn more money with unhosted rentals on the short term rental market than the long term rental market, long term tenants can find themselves evicted to make space for tourists (said, 2014; comiteau, 2016). the availability of peer to peer accommodation leads to critical structural changes at tourist destinations because airbnb changes how tourists are distributed within the destination. tourists use residential areas with inadequate infrastructure for both residents and tourists, putting pressure on regulators in urban (nieuwland & van melik, 2018; sequera & nofre, 2018) and rural areas (gurran et al., 2020) to restrict tourist presence or enable town planners to redesign spaces (palombo, 2015; guttentag, 2017). these regulators lacked funding for such responses (gurran & phibbs, 2017) because, initially, peer to peer accommodation hosts did not pay the same taxes and fees as professional, licensed tourist accommodation providers (kaplan & nadler 2015). given the absence of safety regulations for peer to peer accommodation listings, privately traded spaces can increase risks for guests. commercial, licensed tourist accommodation providers operate in compliance with legal requirements, including building modifications to comply with more stringent health and safety codes and standards (gurran, 2018; gurran et al., 2018). when peer to peer accommodation initially became accessible to the masses, such regulations did not apply to airbnb hosts. many argued that staying at spaces listed on peer to peer accommodation network platforms, therefore, was not safe for tourists (guttentag, 2015; heo, 2016; guttentag, 2017). another challenge for guests is accessibility for those with special needs. policymakers in many countries require commercial, licensed tourist accommodation providers to have a minimum number of accessible rooms. these rooms can accommodate people with special needs, providing, for example, wheelchair access or brail labelling. no such requirements exist for listings on peer to peer accommodation network platforms, although some platform facilitators, such as airbnb, have made attempts to provide more information about the accessibility of properties, allowing people with impairments to better assess the suitability of a space for their needs (randle & dolnicar 2018; 2019; 2021). when regulations for hosts of peer to peer accommodation and commercial, licensed tourism accommodation providers are not the same (koh & king, 2017; hajibaba & dolnicar, 2018) licensed providers feel severely disadvantaged. complying with regulations translates to additional business expenses. licensed premises have to pay tourist taxes, set up accessible rooms, and pay electricians to install and regularly check smoke detectors and carbon monoxide detectors (staley, 2007; guttentag, 2017; dolnicar, 2019). as peer to peer accommodation hosts do not need to pay these expenses, they are able to keep their prices low, giving them a competitive advantage over commercial, licensed providers (davidson & infranca 2018). professional accommodation providers also have to comply with employment standards and regulations. airbnb does not employ its service providers, relying instead on a network of micro entrepreneurs without employment contracts (sigala & dolnicar 2018). the high demand for airbnb listed properties has impacted employment arrangements in the hospitality sector (acevedo, 2016; forgacs & dolnicar, 2018; fang et al., 2016) and reduced daily wages (suciu, 2016). regulatory responses are local in nature; they need to account for the specific circumstances and challenges at a given destination. the best way to illustrate a range of regulatory responses, therefore, is to briefly describe developments in a few locations around the world. we focus on cities located in different continents that have been significantly affected by the rise in popularity of airbnb and similar peer to peer accommodation platform facilitators: amsterdam, barcelona, berlin, hobart, london, new york, paris, reykjavík, san francisco and tokyo. these destinations are not a representative subset of destinations around the world. rather, they are destinations that are very popular among tourists. it is for this reason that they are most affected by the rising popularity of peer to peer accommodation, and have been forced to put in place regulations to minimise negative externalities, while attempting to still harvest the economic benefits for residents offered by short term accommodation. when airbnb started operating in amsterdam in 2008, peer to peer accommodation was unregulated. the lack of regulation did not deter hosts. in 2013, a judge ruled that a homeowner who was renting their property on the short term rental market did so illegally because they were using a residential property as a business. this legal precedent forced regulators to develop a clear position on peer to peer accommodation, leading to the introduction of a regulatory framework in 2014 containing a new accommodation category referred to as private rental. the new framework limited the number of guests to four at a time and limited unhosted renting to a maximum of 60 days per annum. it did not impose a time limit on hosted rentals and spaces covering less than 40% of the host’s primary residence (mou, 2014). the city of amsterdam chose to adopt a collaborative approach with airbnb, a strategy that remains in place today. collaboration is part of the legislation via a memorandum of understanding (mou, 2014). memoranda are renegotiated annually, with all peer to peer platform facilitators contributing to solve newly emerging challenges. collaboration was taken to the next level with airbnb collecting a 5% tourist tax on behalf of the government (airbnb, 2015). but when platform facilitators refused to share their data, the city of amsterdam   independently of airbnb   introduced a mandatory registration system linking hosts to their national identification number (city of amsterdam, 2017). a 2016 memorandum introduced a tool to tackle illegal hotels (mou, 2016), and platform facilitators limited short term rental activity to 60 days per property for hosts without a business licence. airbnb also started promoting its new neighbour tool which residents in amsterdam could use to share specific concerns about a listing, including noise complaints. this was in response to amsterdam residents starting to experience negative externalities, such as changes to their neighbourhood, and rising housing prices. in 2017, it was estimated that 6,000 active listings on airbnb in amsterdam were illegal (van heerde, 2017), leading to a drive to end non compliant short term letting. the agreement negotiated for 2017 and 2018 included new tools to block illegal hosts on trading platforms, and a 24/7 hotline for residents to report delinquent guests or hosts to the city of amsterdam directly. the city of amsterdam and airbnb jointly campaigned to promote local rules, responsible home sharing and how to be a good guest in amsterdam. from 2018, amsterdam increased the tourist tax for peer to peer accommodation to 6%, and capped the number of licences for hosts registered as bed and breakfasts (professional hosted rentals, with no limit on annual days of operation) to 5% per neighbourhood and 25% per building. in 2019, unhosted renting activity was limited to a maximum of 30 days, and the tourist tax increased to 7%. hosts had to notify the city of amsterdam every time they rented out their home. 2020 heralded a new era for peer to peer accommodation in amsterdam. before the covid 19 pandemic, the city council raised the tourist tax to 10% (city of amsterdam, 2020a), and attempted to legally ban short term rental activity in three districts: haarlemmerstraat, kinkerstraat and red light. to achieve the ban, the council had to prove that peer to peer accommodation was endangering residents. in april 2020   despite the reduced number of tourists due to covid 19   the court ruled in favour of the city of amsterdam, banning airbnb from the city centre from 1 july. for all other districts, a licensing system will be introduced (dutchnews, 2020). in response to the covid 19 induced drop in demand, amsterdam temporarily abandoned the system of limited permits for bed and breakfasts (city of amsterdam, 2020b). amsterdam is an interesting case because of its proactive approach to regulating peer to peer accommodation on a continuous basis, and its strategy of working collaboratively with platform facilitators, users and residents. the annual review system ensures that regulation stays up to date and responds to emerging challenges and opportunities. in 2012, when peer to peer accommodation trading commenced in barcelona, short term letting was unregulated. barcelona reacted promptly by introducing a small nightly tourist tax (catalonia news, 2012) and a free registration system. all hosts were added to the tourism registry of catalonia, enabling local councils to monitor the number of listings (portal juridic de catalunya, 2012). the registration process was the same for all accommodation providers: hotels, bed and breakfasts, and peer to peer accommodation hosts. the quick regulatory response is attributed to the level of collaboration and communication between the tourism department of barcelona and barcelona city council. the latter immediately developed a plan to manage tourism for the next three years (insetur, 2014). like amsterdam, barcelona put in place regulations quickly, but with the intention to review and adapt them on an ongoing basis. barcelona’s tourism plan is a key regulatory tool, instrumental in managing tourism in the city and informed by research from the higher institute for tourism research at the university of girona. the aim of the regular tourism planning process is to encourage ongoing debate with all stakeholders in view of achieving the common goal of consolidating barcelona as a key tourist destination in a sustainable and responsible manner. over time, barcelona started to experience a large increase in listings, causing negative externalities to local residents. in response, in 2015, barcelona city council froze the number of tourism licences allocated to short term rentals (espiga, 2014), imposed a €60,000 fine on peer to peer accommodation network platform facilitators listing unregistered properties (lomas, 2016), and created the city and tourism council. experts, citizens, municipal groups, and relevant representatives from barcelona city council collaborated on future tourism proposals (barcelona, 2016). in 2016, the city’s tourism plan granted 9,606 tourism accommodation licences in barcelona, covering both peer to peer accommodation and traditional tourist accommodation. with 15,881 spaces listed that year, 6,275 were illegal (lomas, 2016). improved compliance, therefore, was the focus of the 2017 tourism plan. teams of inspectors started policing the legality of peer to peer accommodation listings. airbnb, in collaboration with barcelona city council, deactivated 1,300 illegal listings (airbnb citizen, 2017), and limited hosting to one property per host in the central barcelona area. barcelona extended the licence freeze indefinitely, and increased the maximum fine for illegal listings to €600,000 (lomas, 2016). in 2018, airbnb agreed to share listing details with city authorities, enabling the identification of illegal listings (o’sullivan, 2018). the city published good practice guidelines for users of peer to peer accommodation in 2019, but kept the number of licences frozen. in the same year, barcelona city council’s 2020 strategic tourism plan included action points to achieve urban balance, improve environmental sustainability, maximise benefits for the destination and involve the entire community. the plan set out to eradicate illegal accommodation (barcelona tourism plan, 2020), and launched the tourism data system operated by the tourism and recreation laboratory at the university of rovira i virgili. the tourism data system records the number of tourists staying in peer to peer accommodation. it shows that more beds are provided by peer to peer accommodation trading platforms than by hotels, and that the number of beds on peer to peer accommodation platforms is increasing rapidly (barcelona tourism plan, 2020). covid 19 will lead to adaptations of the tourism plan. already, barcelona city council has announced a desire to leverage the pandemic to limit short term rentals in view of increasing the availability of long term rentals for residents (burgen et al., 2020). although airbnb started operating in berlin in 2011, the city did not introduce laws regulating peer to peer accommodation until 2014. the 2014 law banned unhosted short term rentals, while permitting hosted short term rentals if the rental surface area covers less than 50% of the floor space. the law is called zweckentfremdungsverbot which translates as the prohibition of using space in unintended ways. property owners can apply for a permit from their local council to engage in unhosted rental activity, but these requests can be refused (zwvbg, 2013; zwvbvo, 2014). airbnb updated its terms of service on their website immediately, however, the law was introduced with a two year transition period. in 2016, berlin state started proactively enforcing the law by introducing a compulsory annual registration system and a €100,000 fine for non compliance. the law defined the limits of short term rentals, but also of unoccupied residential properties and their demolition. the aim of the regulation was to prevent rising property prices and a growing housing shortage in berlin, and immediately led to the removal of 40% of berlin’s airbnb listings (o’sullivan, 2016). to spot illegal listings, berlin relies heavily on residents notifying authorities of non compliant activity. an attempt to force peer to peer accommodation platform facilitators into sharing data with governments failed when a court ruled in airbnb’s favour in 2018. in the same year, berlin state updated the zweckentfremdungsverbot act to explicitly address short term accommodation trading via peer to peer accommodation platforms. according to the updated act, hosts wanting to list more than 50% of their space or their secondary residence on peer to peer accommodation platforms require the permission of the urban development and housing department. this process is free of charge and gives permission for up to 90 days, but is very difficult to obtain because it involves a change of use. the penalty for breaking the rule is €500,000. from 2018 onwards, hosts must publicly display the registration number on the online listing (zwvbg, 2013), enabling inspectors to check compliance. in 2012 when peer to peer accommodation trading started in tasmania, hobart had regulation for traditional tourist accommodation providers and peer to peer hosts, making most private short term rentals illegal by default. four years later, the tasmanian government proposed a 42 day annual cap on unlicensed short term rentals. exceeding this limit required compliance with additional planning regulations (smiley, 2016). public hearings took place in hobart, launceston, and burnie. tasmanian airbnb hosts objected to the 42 day limit (crawley, 2017), which was ultimately abandoned. the revised regulatory framework came into effect in july 2017. under this framework, hosts can list a maximum of four rooms located at their main residence for as many days as they want without a permit (grimmer et al., 2019). in all other cases, hosts must register, pay a $250 registration fee (aird & burgess, 2017) and declare that the space they are listing complies with minimum safety requirements. if the space listed is an investment property larger than 300m2, the property may require a change of use (from residential to visitor accommodation) with the planning office (tasmanian government, 2017). if a change is necessary the space must comply with planning directives, including compatibility with the character and use of the area (planning directive, 2018). in 2019, the state of tasmania introduced an even more drastic change, commencing a data sharing partnership with platform facilitators such as airbnb. the short stay accommodation act legislates that hosts must provide data to platform facilitators. they, in turn, must pass it on to the state government (ssa, 2019). this means that in contrast to the other cities studied (that have their own registration systems), the data from tasmania depends entirely on collaboration with platform facilitators. this is particularly unusual considering that airbnb was suspected of providing incomplete or modified data to the cities of new york (fermino, 2015) and london (ferreri & sanyal 2018). when airbnb started in london, the existing planning laws from 1973 prohibited residential short term rental. users acted illegally for five years before regulators changed the law. the british government showed the first signs of opening up to the idea of peer to peer accommodation in 2013, when they organised a round table of the sharing economy with platform facilitators including airbnb (ferreri & sanyal 2018). in 2014, debbie wosskow, ceo of the online home sharing platform love home swap, was commissioned to make recommendations on how the uk could become the global centre of the sharing economy (wosskow, 2014). a few months later, the first draft of the deregulation act was introduced. the 2015 deregulation act legalised short term rental (deregulation act, 2015). hosts renting out spaces for less than 90 days per year did not require planning permission for unhosted spaces. peer to peer accommodation was also included in the rent a room scheme created by the national government in 1992 as an incentive for people to rent out their spare rooms. under this scheme, the first £4,250 earned per year (uk government, 2018; memorandum, 2015) is tax free, although the payment of council tax is still required. a year later, the national government raised the rent a room tax free threshold to £7,500 (memorandum, 2015; uk government, 2016). in a collaborative effort, airbnb established an annual booking limit of 90 days for unhosted listings on its platform in 2017 (airbnb, 2020). in london, regulation is strict and has not seen much modification over the years. when airbnb started operating in new york in 2008, short term rental was unregulated. the new york city multiple dwelling law enacted in 2010 by the state of new york (nyc, 2010) changed this status, restricting certain types of housing to permanent residence use only. the law mentions an exception for house guests staying for fewer than thirty days, but this wording is ambiguous, creating a loophole for short term renting. in response, the new york city environmental control board proposed an interpretation of the law, saying that hosted listings do not violate the occupancy law. new york state senator liz krueger   the legislation’s primary sponsor   communicated that the law was created to stop landlords from evicting tenants and converting properties to short term rentals (krueger, 2014). yet, many interpreted the new york city multiple dwelling law as a complete prohibition of peer to peer accommodation trading because, under the law, landlords, developers, and residential communities in apartment complexes could prohibit peer to peer accommodation activities in their dwellings. the multiple dwelling law was amended in 2011, and unhosted rentals were prohibited entirely. to list unhosted spaces, hosts required a transient rental permit, involving an inspection by city officials to ensure compliance with strict safety standards encompassing electrical, accessibility, lift, and fire safety measures (ragalie & gallagher, 2014). the law also prohibited the advertising of illegal listings, facilitated the identification of fraudsters, raised fines, and limited hosted peer to peer accommodation to two paying guests at any given time per property (lazarow, 2015). taxation has been at the forefront of regulatory concerns in new york city (interian, 2016). in 2013, new york city started charging a 5.8% hotel occupancy tax, generating 1% of the city’s tax revenue. new york city’s department of finance decided that peer to peer accommodation hosts would not be required to pay the hotel occupancy tax (kaplan & nadler, 2015). brian chesky, one of the co founders of airbnb, expressed his desire to pay occupancy tax in an open letter to mayor bill de blasio (chesky, 2014; tiku, 2014), but the law remained unchanged and only unhosted rentals are required to pay occupancy tax (nyc, 2020). by 2014, despite the strict regulation in place, new york state attorney general eric schneiderman suspected that many listings were non compliant. by going through the courts, schneiderman obtained data from airbnb and other parties (interian, 2016), identifying offences in safety, tax and property law, and a substantial number of illegal listings. between 2010 and 2014, 72% of airbnb listings were illegal (dzieza, 2015). the extent of this illegal activity fuelled objections from the established tourism accommodation sector as well as from residents who were experiencing negative externalities (dzieza, 2015). airbnb reverted to lobbying to avoid prosecution (von briel & dolnicar, 2021), reaching an agreement with new york state attorney general eric schneiderman. hosts, rather than airbnb, would be prosecuted directly for illegal listings. in exchange, airbnb agreed to provide anonymised host data to the attorney general (streitfeld, 2014). the discovery of a large number of illegal listings also led to the refinement of regulations. state governor andrew cuomo increased the fines for illegal short term rentals to us $1,000 for a first offence, us $5,000 for a second offence, and us $7,500 for a third (kokalitcheva, 2016). initially, the fines had a major impact on airbnb’s listings, dropping from 20,000 to 300 (gebicki, 2017). over time, however, the number of listings increased again. in july 2018, the state of new york attempted to amend the multiple dwelling law again with a bill to regulate short term rentals (the new york state senate, 2018). the bill states that short term online accommodation platform facilitators must provide monthly transaction data. airbnb, expedia and homeaway united to successfully fight the bill, which was blocked by a federal judge just before coming into effect (weiser & goodman, 2019). despite this victory, airbnb immediately commenced settlement negotiations with the new york city, agreeing to share the anonymised data of illegal hosts (von briel & dolnicar, 2021), and more specific data upon request (airbnb and nyc agreement, 2019). despite airbnb’s collaborative attitude, the government suspected that data provided by airbnb in the early years was incomplete, and called for more control (fermino, 2015). today, post covid 19, there are over 50,000 airbnb listings in new york city, illustrating how the market has adapted to severe restrictions. paris immediately became a successful market for airbnb when it started operating there in 2012. negative externalities soon emerged, however, french regulators adopted a wait and see approach. in 2014, paris was the number one airbnb destination in europe, and second in the world. the french government introduced national regulation (alur law, 2014), which could be considered relatively moderate (o’driscoll, 2015). for a short term rental to be legal, the new regulation prescribed that it must be the host’s primary residence (where they reside for at least eight months of the year), it cannot be rented out for more than 120 days annually, and must be furnished. those wanting to rent out their primary residence for longer, or rent out a secondary residence, must register a commercial property. in 2015, peer to peer accommodation platform providers and the city of paris started to collaborate. airbnb collected and remitted a nightly tourist tax for the french government; the same tax paid by hotels (de cardona & putois, 2015). to increase compliance, the french government amended the law, forcing hosts   from january 2017 onwards   to register their listings with their local city hall and obtain a certification number (alur law, 2016). hosts who convert residential properties into commercial properties must purchase commercial properties of the same floor area and convert them into residential properties. airbnb demonstrated its collaboration with the french government by listing only registered properties. a law introduced in 2018 allows municipalities to set annual caps in view of local requirements, imposes heavy fines on noncompliant hosts, forces hosts to disclose their records to the council upon request, and makes permission of building co owners compulsory before listing a property (elan law, 2018). as of 2020, online platform providers must provide yearly rental data to the french tax authorities, including the host’s name, date of birth, and address, any sales made via the platform (for experiences or accommodation), and the payment method used. the french government aims to have these new systems fully functional by the 2024 paris olympics, when demand for temporary housing will skyrocket. in response to the dramatic increase in tourist numbers (statistic iceland, 2020), the icelandic government introduced strict regulation for short term rentals in 2007, long before airbnb entered the market in 2012. in 2015, the annual number of international tourists visiting iceland reached one million (icelandic tourism board, 2016), and the number of properties listed on airbnb was estimated to have increased by 124% in a year (davies, 2016). this additional accommodation capacity was very welcome at the time, as both reykjavik and iceland wanted to benefit from the increased tourist demand (adamiak, 2018; cvelbar & dolnicar, 2018). despite the strict short term rental regulations introduced in 2007, approximately 10% of tourists visiting iceland in 2016 stayed in private accommodation (icelandic tourism board, 2017), and residents started experiencing a shortage of long term rental properties and an increase in real estate prices (davies, 2016; elíasson & ragnarsson, 2018). the icelandic government responded by putting new regulations in place in 2017. under a new law, hosts cannot rent out spaces for longer than 90 days or earn more than two million icelandic króna a year in combined gross rental income (iceland government, 2016). the national government also introduced a compulsory annual registration fee of 8,000 icelandic króna (iceland government, 2016). each registered host is issued with a unique number to display on their airbnb profile, and must declare each guest staying at a property to the district commissioner. any rental activity exceeding the 90 day limit, involving the hosting of more than ten people at the same time, or involving more than five rooms, is only legal if the host registered a business (comiteau, 2016) at a cost of between 32,000 and 263,000 icelandic króna (depending on whether alcohol and food were provided). despite the new law, up to 70% of airbnb listings were non compliant (icelandmag, 2017). homestay patrol was established to check compliance, and the law was amended requiring all short term rentals to be operated as a business. the registration fee was raised to 9,060 icelandic króna. these regulations remain in place. iceland does not collaborate with airbnb on tax collection or the capping of listings to a maximum number of days (homestay patrol, personal communication). when airbnb launched in san francisco in 2008, short term rental was forbidden by law. however, airbnb offered job opportunities in the middle of a financial crisis, which attracted supporters among members, the local san francisco workforce and politicians, including mayor ed lee. airbnb launched several lobbying campaigns to unite supporters (von briel & dolnicar, 2021). housing activists, led by the city attorney of san francisco dennis herrera, argued that short term rentals removed housing stock from a rental market that was already struggling. no regulation was introduced for many years. in 2014, politicians opposed to airbnb created draft legislation (referred to as proposition f) to protect tenants facing eviction because of peer to peer accommodation. a legal battle erupted and both parties used the municipal elections to present their positions. the candidate in support of airbnb, ed lee, won the 2015 elections and became mayor. lee immediately introduced a new short term rental law that legalised airbnb (hamilton & romney, 2015). the law required hosts to register as a business and pay a registration fee, to complete a biannual registration with the office of short term rentals at a cost of us $250, and to pay 14% transient occupancy tax (airbnb, 2017; san francisco business portal, 2017). with respect to safety, hosts must inform guests   using a sign at the premises  where fire exits, fire alarms, gas shut off valves, and fire extinguishers are located. the new law did not impose a time limit on hosted rentals, but limited unhosted rentals to 90 days (airbnb, 2017; san francisco business portal, 2017). hosting activity was only legal if it occurred at the host’s primary residence; the place they live in for a minimum of 275 days every year. many hosts initially ignored the new law, refusing to register their short term rental activities. an attempt to impose a fine of $1,000 per day on peer to peer accommodation platform facilitators for illegal listings was challenged in court by airbnb and homeaway. their case was rejected (levine & somerville, 2016). in 2016, a new regulation was introduced, requiring hosts to submit quarterly activity reports to the office of short term rentals of san francisco. after a year of legal battle, in 2017, airbnb and regulators reached a collaboration agreement. the city abandoned the fine for illegal listings, and airbnb committed to listing only registered properties (farivar, 2017). airbnb created and launched a pass through registration system that allows hosts to apply for a short term residential rental certificate and business registration certificate via its platform. airbnb also became the only qualified website company for short term rentals in san francisco, automatically collecting occupancy taxes and remitting them to the city. hosts using other platform providers (e.g. homeaway, vrbo) are individually responsible for calculating, collecting, and remitting these taxes to the city for each booking, and have to apply for the short term rental and business certificates. the level of collaboration between the city of san francisco and airbnb is unequalled in the world, but the legal battle with opponents of airbnb continues. despite previous efforts to eradicate illegal short term rentals, the city attorney of san francisco issued fines of us $5.5 million to hosts of illegal airbnb listings, fuelling criticism against peer to peer accommodation platform facilitators. in 2019, a modified proposition f was approved, limiting the size of financial contribution a company can make to a political campaign, and the level of ownership a politician can have in a company (san francisco department of elections, 2019). this regulation was targeted at san francisco politicians supporting airbnb. the office of short term rentals raised the biannual registration fee to $450 to finance their activities and the required adaptations to local neighbourhoods (to enable them to better cater to peer to peer hosted tourists). the city of san francisco announced new legislation, introducing a requirement for planning approval of new buildings intended for intermediate stays (waxmann, 2019). the legislation targeted a new range of hospitality professionals using peer to peer accommodation for corporate travellers. these listings are rented out for longer than 30 days, yet effectively operate as hotels and remove housing stock from the residential market. this regulation was deemed necessary because airbnb and a new start up   sonders   had invested in companies and buildings in san francisco to pursue the business traveller market (von briel and dolnicar, 2021; burke, 2019). once more, regulators had taken years to react and struggled to catch up with market changes. in 2020, covid 19 changed airbnb’s business traveller strategy. the new legislation   combined with the pandemic   is likely to lead to investment properties being transitioned (back to) the long term rental market. japan has a tradition   called minshuku   of guests staying at an owner’s home, sharing meals, facilities and lifestyle together. minshuku has been regulated in japan since the second world war under the hotels and inns act (umeda, 2017). all short term rentals in japan were subject to this law before airbnb started operating (nippon, 2016). consequently, when airbnb entered the tokyo market in 2012, its activities were illegal. this regulatory status quo lasted for five years. in 2016, airbnb launched a lobbying campaign in tokyo (von briel & dolnicar, 2021). the parliament legalised airbnb in 2017, and introduced the home sharing business act in 2018 (umeda, 2017). short term rentals became legal for up to 180 nights per year. the system is called minpaku (private lodging) and is regulated by local councils (minpaku, 2020). the extensive registration system includes mental health and fire safety checks. all transactions must be recorded and shared online with the local council. in tokyo, each council rules separately and differently. some have banned short term rentals completely, while others, such as chuo ward, allow rentals exclusively in school holidays to protect children from strangers (japan property central, 2018). in 2018, airbnb stopped advertising listings that were not displaying a minpaku registration number, causing an 80% drop in tokyo’s airbnb listings (johnston, 2018). in preparation for the olympic games (initially scheduled for 2020), tokyo developed a tourism plan which included sustainable hosting solutions. airbnb partnered with the international olympic committee, guaranteeing accommodation for visitors, families of athletes, and officials during the games (asher hamilton, 2019; fairley et al., 2021b). this agreement avoided the need to construct new accommodation to meet demand during the event. the city of tokyo is another example of how a market can adapt and flourish despite tough regulations. many studies have described the regulatory responses to airbnb at specific locations at a single point in time (e.g. lee, 2016; wegmann & jiao, 2017; hajibaba & dolnicar, 2018; crommelin et al., 2018; nieuwland & van melik, 2018). the evolution of regulatory responses remains under investigated, despite modifications over time being informative for destinations that are in the process of developing regulations. the longitudinal perspective provides insights into successful and unsuccessful attempts to achieve set goals, using specific measures. one such analysis monitors regulatory action for 11 cities: amsterdam, barcelona, berlin, hobart, london, new york, paris, reykjavík, san francisco, tokyo and vienna (von briel & dolnicar, 2020). technical innovations such as airbnb can potentially entice four types of disruptions on regulations: end runs, when an existing law equivocally covers the innovation; exemptions when the innovation has no negative impact and regulators simply ignore it; gaps when no regulation covers the innovation; and solutions, when the innovation is solving a regulatory issue (biber et. al, 2017). the cities under investigation suffered negative consequences from airbnb. as a consequence, they found themselves either in an end run or gap situation. regardless of the starting point, all cities converged to a similar solution, as illustrated in figure 4.1. figure 4.1 adapts the point system introduced by von briel and dolnicar (2020) for toughness of regulation. we changed the point system to  10 for a gap situation, 0 for a moderate regulation, 10 for end run, and graded each city each year. we calculated the mean of all end run cities each year and the mean of all gap cities each year and illustrated the result on a line chart. the two dominant trajectories are illustrated in figure 4.1. the figure illustrates how, over time, all end run cities became more lenient, while all gap cities start unregulated and introduce more regulation over time. by 2020, all of the cities studied find themselves in a position of moderately strict regulation. to measure how hosts perceive regulations which affect their peer to peer accommodation trading activity, we surveyed 57 hosts in january 2021. to be eligible to participate in the survey study, respondents had to have listed accommodation on an online peer to peer accommodation platform in the last 24 months. we asked respondents if they had proactively sought out information regarding local regulations for short term accommodation before listing their space on an online platform. in our study, 86% of respondents stated that they did check local regulations. this appears to contradict the high numbers of illegal listings reported in popular tourist destinations. a potential explanation could be that only a small number of hosts are listing illegally but that these hosts list multiple properties. another interpretation is that hosts are aware of the regulations under which they operate, yet some choose not to comply with them. figure 4.2 shows regulatory changes in response to covid 19 as perceived by the hosts surveyed in january 2021. most hosts (49%) reported that the regulations that guide their airbnb hosting activities have changed. of those, 75% reported that they have been impacted by stricter regulations; 25% reported that they have not. one third of hosts reported that regulations remained unchanged during covid 19 compared to before covid 19, and 19% of hosts confessed that they do not know whether or not regulations have changed. covid 19 interrupted the increase in the trading of short term rentals between ordinary people using online platforms such as airbnb.com. with borders closed, major restrictions on travel and the requirement to socially distance, hundreds of thousands of airbnb listed spaces suddenly stood idle. some of airbnb’s biggest markets   europe and the united states ­  were at the covid 19 epicentre in march 2020 (adhanom ghebreyesus, 2020; rettner, 2020). by august 2020, airbnb founder brian chesky announced that the company had already lost us $1 billion due to lost bookings (crane, 2020). this financial loss increased further when airbnb agreed to compensate hosts for covid 19 related cancellations (fleetwood, 2020). chesky voluntarily took a pay cut of us $250,000, and 25% of airbnb staff had to be laid off. as soon as lockdowns were lifted, chesky reports, booking numbers on airbnb.com increased to numbers higher than those seen in similar periods in 2019. this increase did not occur across all location types. rather, demand in city locations dropped, while low population density regional areas   considered by travellers as safer during the pandemic   became extremely popular (transparent, 2020). interestingly, according to a report from airdna, bookings for home rentals have recovered faster than those for hotels (press, 2020). in response to the challenges experienced in 2020, chesky rethought airbnb’s future strategy, concluding that it would focus on independent hosts (as opposed to large operators) and put more emphasis on developing listings in less populated areas (crane, 2020). both of these initiatives limit the negative externalities that popular city destinations tend to experience, and maximise the economic benefits from airbnb for regional communities in desperate need of employment opportunities. post covid 19, airbnb may well find itself moving back to its roots; the trading of space in people’s primary residences (dolnicar & zare, 2020). chesky’s change of strategy may also reflect the increasing inclination of popular city destinations to tighten short term letting regulations   a major risk to the company’s growth (vinogradov et al., 2020). the deputy mayors of barcelona and paris   janet sanz and ian brossa   applauded this change and expressed their desire to boost the long term housing market with empty short term rental properties (burgen et al., 2020). this is in line with a broader trend of policy makers leveraging covid 19 to reclaim their cities and fight the gentrification caused by the large scale trading of peer to peer accommodation (bevins, 2020; dutchnews, 2020). covid 19 may well lead investors   who were attracted to the short term rental market by the prospect of a higher return on investment   back to the long term rental market to minimise the risk of significantly reduced returns (as predicted by dolnicar & zare, 2020). chesky’s comments reflect these predictions; he expects that (1) the travel market will never go back to what it was; (2) the business travel market will shrink in size dramatically because people have realised they can achieve the same results without travelling; and (3) people will want to travel locally, by car, to nature, with people they care about (chang, 2020). it will be interesting to see if the new focus on regional locations increases economic benefits, or shifts some of the challenges residents of cities have experienced to regional areas. this may depend on whether the regional locations are, in their own right, popular tourist destinations, or regional areas suffering from a lack of employment and business opportunities. as for airbnb, its revenue is increasing again as travel restrictions are relaxed. the company has regained its confidence and, in august 2020, announced that it would file to go public (griffith, 2020). regardless of regulations and potential new regulatory challenges, airbnb sees a bright future ahead. this chapter is based on 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an official website of the united states government official websites use .gov a .gov website belongs to an official government organization in the united states. secure .gov websites use https a lock ( lock locked padlock icon ) or https:// means you've safely connected to the .gov website. share sensitive information only on official, secure websites. primary site navigation logged in as: corresponding author. school of business and economics, universiti putra malaysia, serdang, malaysia. wcchoo@upm.edu.my received 2023 mar 6; revised 2023 jun 1; accepted 2023 jun 7; collection date 2023 jun. this is an open access article under the cc by license (http://creativecommons.org/licenses/by/4.0/). this study employs advanced text mining techniques to offer an in depth and comprehensive overview of the extensive body of research on airbnb. by analyzing 1021 articles published in 416 journals spanning the period from 2015 to 2022, this study aims at revealing airbnb research topics and trends. the results show that the primary focus of academic inquiry regarding airbnb revolves around two domains: the company's operational practices and its impacts on various domains. within the realm of airbnb's operational practices, four distinct research topics emerge as particularly prominent and extensively explored. these encompass the dynamics of ‘trust in airbnb,’ the formulation and implementation of ‘house rules,’ the mechanisms of governing ‘airbnb pricing’ strategies, and the critical examination of ‘value creation in airbnb’ initiatives. meanwhile, the most researched impacts of airbnb are on urban tourism, rental housing markets, tourist destinations, and hotels. these spheres have received significant scholarly attention due to the profound implications and transformative effects engendered by airbnb's disruptive presence in these areas. moreover, the findings underscore that research pertaining to airbnb's operational aspects has witnessed a significant increase in popularity over time, indicating a marked shift in the focal points of airbnb research. notably, the research topics that have experienced substantial growth include ‘trust in airbnb,’ ‘airbnb pricing,’ and ‘impacts on tourist destinations.’ lastly, this study found that airbnb related research articles in hospitality and tourism journals tend to be more delving into industry specific phenomena and challenges. conversely, non hospitality and tourism journals provide a broader coverage of topics related to airbnb, encapsulating diverse areas of inquiry beyond the boundaries of the industry. this literature review provides valuable insights into existing research on airbnb and highlights several critical areas for future research. keywords: airbnb, sharing economy, peer to peer accommodation, text mining, topic modeling, sustainable tourism the sharing economy has been one of the most significant developments in recent years in the field of electronic commerce. it refers to an economic model where individuals share their resources, such as homes, cars, and assets, with others for financial gain [1]. the widespread adoption of mobile devices and ubiquitous internet connectivity has fueled the rise of sharing economy platforms. by leveraging digital technologies, the sharing economy creates new marketplaces that connect users directly with service providers, bypassing traditional intermediaries. this decentralized business model has gained popularity not only for economic reasons but also for its potential to create sustainable and environmentally friendly communities. by reducing waste, promoting the efficient use of resources, and decreasing the environmental impact of goods and services, the sharing economy model contributes to a better future [2]. one of the most well known examples of a sharing economy company in the accommodation sector is airbnb. established in 2008, airbnb has revolutionized the industry by providing a platform for individuals to rent out their homes or rooms to travelers. today, it is one of the largest home sharing platforms globally, operating in over 220 countries and regions [3]. airbnb offers property owners the opportunity to list their properties and provides travelers with an extensive selection of unique and affordable accommodations [4]. it has hosted over 800 million guests to date [5]. however, the covid 19 pandemic had a significant impact on airbnb's business, leading to a sharp decline in bookings due to travel restrictions and lockdowns. however, as restrictions began to ease in some regions, airbnb experienced a surge in demand for local and domestic travel. airbnb's transformative impact on the travel industry, sharing economy, and the global economy is evident in its widespread popularity and the numerous academic studies examining its influence. researchers have identified several factors that drive customers to adopt airbnb services, with low cost being a primary motivator [[6], [7], [8]]. airbnb offers guests a wide range of rental options, including entire places, private rooms, hotel rooms, and shared rooms, which can provide cost saving options for solo travelers who opt for shared properties. in addition, group travelers can save money compared to traditional hotels. a nerdwallet analysis revealed that the average airbnb rental for six people was 33% cheaper than booking three hotel rooms [9]. additionally, numerous airbnb listings offer fully equipped kitchens, which can help guests save money on meals. the pursuit of an authentic local experience is another key motivator for many guests to choose airbnb accommodations. airbnb's “live like a local” approach offers guests the opportunity to stay in properties that are managed and designed by residents, resulting in unique and authentic experiences [10]. this enables guests to gain an unconventional understanding of the local culture and daily life in their destinations [7]. social interaction is also a significant aspect of the local experience, as airbnb guests have the chance to interact with hosts, neighbors, and other guests, creating unforgettable experiences and social benefits [11]. extensive academic research has focused on the disruptive impact of airbnb on the hospitality industry and housing market. several studies have reported mixed findings on the effect of airbnb on hotel performance, with some indicating a negative impact on hotel revenue in specific cities such as barcelona [12], paris [13], and london [14], while others have found no significant impact on hotel revenue [15,16]. additionally, it has been argued that airbnb accommodations mostly substitute mid range hotels, indicating that not all hotel categories are directly affected [17]. apart from its impact on the hospitality industry, airbnb has also been found to contribute to the increase in rental prices in cities with high airbnb density [[18], [19], [20]]. converting long term rentals into short term rentals for tourists reduces the supply of available housing, leading to a rise in rental prices [21]. moreover, the number of commercial airbnb hosts entering the business is increasing, creating competition for high quality properties in the housing market, and driving up rents. the impact of airbnb on housing markets has raised criticism, leading some cities to impose regulations on short term rentals to address concerns about safety, zoning, and affordability [21,22]. despite the criticisms, airbnb remains a significant player in the sharing economy and is likely to play a vital role in shaping the future of travel and tourism. while airbnb has caused disruptions, its significant contributions to the growth of the global tourism industry should not be overlooked. offering tourist accommodation, it enhances the economic impact of tourism on local communities and residents. tussyadiah and pesonen [23] suggested that airbnb increases the number of destinations chosen, the length of stay, and the number of activities pursued, thereby maximizing tourism spending. in addition, airbnb plays a crucial role in attracting tourists to a city through the online behavior of hosts and guests. airbnb hosts often attract potential guests by showcasing images of the neighborhood that guests will visit. airbnb guests also contribute to creating images of tourist destinations. a statistic shows that between 2008 and the covid 19 pandemic, airbnb guests provided over 24,000 descriptions of about 500 urban neighborhoods across cities in the northern hemisphere [24]. moreover, airbnb creates entrepreneurial opportunities for property owners, who can use it as a low risk platform to gain early career experience and generate additional income. fischer et al. [25] revealed that financial gain and economic independence are important motivators for individuals to become micro entrepreneurs in the sharing economy. as research on airbnb continues to evolve, efforts have been made to review the existing literature [[26], [27], [28]], which provided some understanding of the research status of airbnb. however, many of these studies relied on conventional qualitative analysis methods to analyze text data. qualitative analysis of literature is time consuming, and researchers often compromise the depth and rigor of their reviews by limiting the number of journals and years included [29], or excluding relevant articles from related fields, which can have serious implications for their results and contributions. moreover, manual coding can be subject to subjective bias, as researchers decide what to include and exclude in a topic, which can lead to challenges in interpretive bias and coding objectivity [30]. traditional methods also limit large scale reviews of airbnb literature. additionally, most previous studies only sample journals from the hospitality and tourism fields, potentially overlooking important insights from non hospitality and tourism journals. this study aims to bridge the gap in the literature by conducting a systematic review of articles on airbnb published in both hospitality and tourism and non hospitality and tourism journals using text mining techniques. text mining has been widely recognized for its ability to synthesize information and identify emerging trends in research topics [[31], [32], [33]]. unlike manual analysis, text mining provides a scientific basis and eliminates the subjectivity that can arise from group experts [34]. additionally, text mining allows for the efficient processing of large amounts of text documents, which enables researchers to analyze a larger sample size of literature. this study specifically utilized the structural topic model (stm) tool developed by roberts et al. [35] to identify key research topics related to airbnb. this study attempted to address the following research questions: rq1: what are the prominent topics that are discussed in research concerning airbnb? rq2: how do the research topic distributions vary across hospitality and tourism journals and non hospitality and tourism journals? rq3: how does the popularity of identified research topics change over time? this study contributes to the airbnb research domain by offering a comprehensive understanding of the prominent research topics and trends in this area. this contribution enables academic researchers to identify recent research directions and thereby advance the state of knowledge in this field. this study utilizes both traditional research synthesis and integration methods, as well as a novel approach based on the stm technique, to extract and visualize potential research topics and their evolution over time. the innovative stm approach discussed in this study delves deeper into the research literature and extracts research topics in an automated data driven manner. the present study is organized as follows: section 2 describes the search criteria used to identify relevant research articles on airbnb, including the inclusion and exclusion criteria, and provides a detailed explanation of the research procedures employed. in section 3, an overview of the identified articles is presented in terms of their publication journals, year of publication, and thematic coverage, along with an exploration of the interrelationships between various concepts. section 4 provides a thorough discussion of the findings and implications. section 5 concludes this study, offering a critical evaluation of the limitations encountered and providing valuable recommendations for future research directions. this article adopts a systematic literature review methodology, as indicated by gupta et al. [36], to analyze literature on airbnb published in both hospitality and tourism journals, as well as non hospitality and tourism journals. the scopus database was selected as the search tool due to its broader coverage of related literature compared to the commonly used web of science. the authors used search keywords such as “airbnb,” “peer to peer (p2p) accommodation,” and “sharing economy” in the title, abstract, or keywords to retrieve relevant articles. after manual filtering of articles without abstracts and those not related to the accommodation industry, the authors collected 1021 articles published between october 2015 to october 2022. the scopus database was the primary data source for this research, and a comprehensive excel file containing research titles, journal titles, abstracts, keywords, publication dates, and author affiliations was downloaded and stored for analysis. fig. 1 outlines the research procedures employed after data collection. the first step is text pre processing, which involves converting all text to lowercase and removing special characters such as punctuation, numbers, and white space that are irrelevant to the text mining analysis. a set of commonly used english words, also known as stop words, are eliminated from the document using the smart lexicon in the “tm” package to focus on words that are more meaningful to researchers. in addition, a customized list of stop words, such as publisher names and abstract sub titles, is utilized to remove any remaining stop words. the words are then lemmatized to normalize different word forms to a common base form, reducing the corpus's dimensionality. the advantage of lemmatization over stemming is that it considers the context of a word to determine its intended meaning, avoiding over stemming that could result in the loss of important information and undesirable interpretability [37]. the abstracts serve as input for topic modeling, and frequent bigrams in the abstracts are concatenated. in step 2, the term frequency method is used to analyze the article titles and keywords. the frequency distribution of unigrams, bigrams, and trigrams is computed, and a word cloud technique is utilized to show frequently occurring words in the documents. the larger the font in the word cloud, the more frequently the word appears in the document, and vice versa. research procedures. source: created by the authors. in step 3, this study utilizes stm topic modeling to identify major research topics on airbnb by analyzing abstracts of selected articles. stm is a unique type of topic modeling specifically designed to meet the research needs of social scientists. it provides the ability to integrate metadata into the model and to discern how different documents express a shared underlying topic using varying lexical constructs [38]. as a valuable tool, stm provides several advantages for literature reviews. it enables the identification and exploration of latent topics in a given corpus, facilitating the discovery of themes and patterns that may not be immediately apparent through traditional methods like qualitative reviews. additionally, stm allows for the incorporation of document level metadata as covariates [35], which helps examine the relationship between topics and external factors such as publication year, author, and document type. in this study, the covariate is the field of the journal, i.e., hospitality and tourism, and non hospitality and tourism. stm's framework is highly flexible, making it possible to analyze large and complex datasets, including handling missing data. finally, stm enables the estimation of topic prevalence over time, making it possible to explore how research topics on airbnb evolve and change over time. fig. 2 depicts the generative model, which starts by considering a document, represented by d ∈ 1 … d, and the words contained within the document, denoted by n ∈ 1 … nd. these words, represented as wd,n, are treated as unique terms from a vocabulary of size v, indexed by v ∈ 1 … v. additionally, the model incorporates k topics, where k ∈ 1 … k. two design matrices, x and y, provide further observed information regarding the topic prevalence and topical content, respectively. researchers can specify these matrices based on a document and its associated covariate vector for each row. the dimension of x is d × p, whereas y has a dimension of d × a, and their respective rows are denoted as xd and yd. finally, the model considers the marginal log frequency of term v in the vocabulary, which is estimated from total counts, and denoted as mv. illustration of the structural topic model. source: adopted from roberts [38]. the generative process of a document in a stm model with k topics can be described using four scalar hyper parameters (s, r, ρ) and a k dimensional hyper parameter vector (σ) as: where the logistic normal distribution is utilized by the core language model to establish associations among topic proportions. for a model comprising k topics, the logistic normal distribution is denoted by ηd ∼ normalk−1 (μd, σ) and mapped onto the simplex with θd,k=exp⁡(ηd,k)/(σi=1kexp⁡(ηd,i)). in order to ensure model traceability, ηd,k is set to zero. for every word in document d, a topic is randomly selected from the multinomial distribution zd,n ∼ multinomial (θd), based on the given topic proportion vector, θd. the word is chosen from the distribution of terms bzd,n, conditioned on the topic and written as βzd,n for simplicity. table 1 displays a ranking of the top journals that published airbnb related studies, with the number of appearances and percentage of total appearances listed. the international journal of hospitality management appears the most, with 75 appearances, representing 18% of the total. the international journal of contemporary hospitality management, sustainability (switzerland), and tourism management all have 36 appearances, representing 8.8% of the total. the annals of tourism research has 27 appearances, representing 6.4% of the total, while the journal of hospitality and tourism management has 18 appearances, representing 4.3%. the remaining 11 journals have between 11 and 14 appearances, representing between 2.6% and 3.3% each, and the “others” category represents 48 appearances or 11.5% of the total. among the journals that published more than 10 articles related to airbnb, sustainability, environment and planning a and journal of business research are not under the category of hospitality and tourism. notably, several journals that have published over 10 articles on airbnb, such as sustainability, environment and planning a, and journal of business research, are not classified as hospitality and tourism journals. this suggests that research on airbnb extends beyond the hospitality and tourism industry and has broader implications for sustainability and business research. number of selected articles by journal. note: the journals shown in the table refer to the samples collected in this study from the scopus database. based on fig. 3, there has been a remarkable increase in the annual quantity of academic articles focusing on airbnb over the years. this growth has been particularly significant since 2016, with a peak of 249 publications in 2020, followed by a slight decline in the following two years. the rise in academic interest in airbnb can be attributed to the rapid growth and expansion of the sharing economy, in which airbnb plays a prominent role. scholars from diverse fields, including business, economics, and hospitality, have been increasingly interested in exploring the impacts of the sharing economy on various aspects of society. the decline in airbnb related publications after 2020 may be attributed to the pandemic's effect on the travel and tourism sector. due to the widespread disruption of travel and tourism activities, including border closures, lockdowns, and restrictions on public gatherings, research on airbnb and its implications may have been deprioritized or delayed. nevertheless, given the enduring significance of the sharing economy and its potential for growth and disruption, researchers are likely to continue investigating airbnb's impact on various domains of society in the future. article count trend. source: created by the authors. n gram analysis is a statistical method used to detect repeated word patterns in a body of text [39]. table 2 displays the result of an n gram analysis of airbnb research keywords. the most frequent unigram is “tourism,” indicating that airbnb research places significant emphasis on the tourism industry. this is followed by “analysis,” “industry,” “market,” and “tourist,” which suggest that airbnb is interested in examining the tourism industry and market. the prevalence of terms such as “social,” “behavior,” and “urban” suggests that airbnb research also focuses on the social and behavioral aspects of tourism, particularly in urban areas. the most frequent bigrams and trigrams provide further insight into airbnb's research focus. for instance, “tourist_destination” and “tourism_economics” indicate airbnb's interest in the economic consequences of tourism and how tourists select their destinations. “hospitality_industry” and “hotel_industry” suggest that airbnb is also interested in the competition between traditional hotels and the sharing economy. furthermore, the presence of terms such as “world_wide_web,” “distributed_computer_systems,” and “human_computer_interaction” suggests that airbnb research also investigates the technological aspects of the tourism industry. the frequent use of location specific terms such as “new_south_wales,” “ile_de_france,” and “ville_de_paris” indicates that airbnb research explores the regional variations of tourism. n grams analysis of research keywords. note: the keywords listed in this table are the specific terms and phrases extracted from the articles selected from the scopus database for this study. table 3 displays the frequency of n grams in a corpus of airbnb research titles. the most frequent unigram is “airbnb” (502 occurrences), which suggests that the majority of research focuses on airbnb as a platform. the second most frequent unigram is “sharing” (226 occurrences), highlighting the importance of the sharing economy concept in research. the most frequent bigram is “sharing_economy” (172 occurrences), emphasizing the significance of this concept. the second most frequent bigram is “airbnb_listings” (22 occurrences), indicating that research has also explored the characteristics of airbnb listings. regarding trigrams, the most frequent is “new_york_city” (nine occurrences), suggesting that new york city is a popular location for airbnb research. the second most frequent trigram is “sharing_economy_platform” (five occurrences), indicating an interest in studying different platforms that are part of the sharing economy. other frequent terms in the corpus include “economy,” “accommodation,” “tourism,” “platform,” and “case,” demonstrating the different aspects of airbnb research that have been explored. the results suggest that the sharing economy and airbnb as a platform are major areas of interest in the research community. moreover, the frequent occurrence of terms related to accommodation and tourism suggests that research has investigated the impact of airbnb on these industries. n grams analysis of research titles. note: the terms and phrases presented in this table are taken from the article titles that were selected from the scopus database for this study. in the context of stm topic modeling, the selection of an appropriate number of topics, denoted by k, is critical for ensuring the reliability and accuracy of the results obtained. previous studies have suggested that both quantitative and qualitative criteria should be employed in the selection of the optimal number of topics [38]. the quantitative criterion for determining the optimal number of topics is based on a trade off between two statistical metrics: topic coherence and exclusivity. topic coherence is a measure of the semantic relatedness between words that tend to appear in the same topic, and it is estimated by computing the sum of the log ratio between co document frequency and the document frequency for the most likely words within a topic [40]. this metric assumes that words that are semantically related are likely to co occur frequently within the same document. exclusivity, on the other hand, is a measure of the distinctiveness of different topics, and it is computed by comparing the similarity of the word distributions of different topics [35]. high exclusivity indicates that more words belong exclusively to a specific topic. despite the benefits of using quantitative criteria to determine the optimal number of topics, it is not always consistent with the choices made by human coders [41]. therefore, researchers are encouraged to conduct a qualitative examination of the relevance and interpretability of the identified topics within their specific research context. we examined the topic coherence and exclusivity of different topic model solutions within the range of 10 40 topics. fig. 4 illustrates the results; the topic coherence on the left and exclusivity on the right, with the smaller semantic coherence values indicating better topic model performance, and larger exclusivity values indicating better topic model performance. however, none of the topic models demonstrated superior performance on both statistical measures. we also examined the interpretability of the models for topic models ranging from 18 to 28 to determine the most suitable number of topics. after thorough analysis, we determined that the optimal number of topics was 20. this decision was based on the fact that beyond 20 topics, there was no significant improvement in both topic coherence and exclusivity. moreover, the model with 20 topics had more interpretable topics and fewer overlapping topics. the semantic coherence and exclusivity of 40 topic models. note: the semantic coherence score measures the degree of semantic similarity among the topics generated by the model. the exclusivity score quantifies the exclusiveness of topics, highlighting the extent to which a particular topic is distinct from others. source: created by the authors. the labeltopics function, a part of the “stm” package in r language, enables the labeling of identified topics by creating a comprehensive list of topic related terms with varying degrees of importance, as defined by four distinct weightings: highest probability, frex, lift, and score. of these weightings, the highest probability and frex rankings were primarily utilized to identify the most appropriate labels for each topic, as they reflect the most frequent and distinctive terms associated with a given topic. to ensure the accuracy and reliability of the labeling process, the final selection of each label is subjected to a rigorous verification process. this involves a comprehensive evaluation of each candidate label based on its relevance, specificity, and accuracy in capturing the main themes and patterns within the topic. to better understand the context of the top words that appear in each topic, the findthoughts function was used to generate a representative sample of text. this sample can help researchers identify the main themes and patterns within each topic and create a list of potential labels. the final decision on the most appropriate label in this study is made through a collaborative process among the researchers, where consensus is reached through discussion and deliberation. fig. 5 illustrates a ranking of the extracted topics based on their proportions. the proportions refer to the percentage of total occurrences of each topic in the dataset. each topic is represented by a number, and the figure also displays the three most frequently occurring words within that topic. the quality of each topic is assessed by plotting semantic coherence and exclusivity scores, illustrated in fig. s1 (supplementary material). among the 20 topics, ‘technological innovation’ scored lowest for exclusivity, and ‘airbnb cybersecurity’ scored lowest for semantic coherence. hence, more attention should be paid when interpreting these two topics. the resulting labels for each topic and the top words ranked by the highest probability and frex metrics are presented in table 4. ranking of topics by proportions. note: the ranking presented in this figure is determined by the expected proportions of the identified topics, which indicates the relative significance and prevalence of different topics within the analyzed dataset. source: created by the authors. topic summary. note: the highest probability words are the terms that are most likely to be connected with a particular topic. frex is a measurement that considers how often words appear in a specific topic and how unique they are to that topic compared to others. in this section, we aim to provide an explanation of each topic by referring to the top words and prior studies. topic 1 focuses on the trust mechanism of airbnb, which is closely related to user satisfaction and loyalty behavior. the keywords associated with this topic include “trust,” “satisfaction,” and “repurchase.” previous researchers investigated the relationship between trust and user satisfaction and loyalty behavior. chuah et al. [42] found that perceived trust has a positive impact on user satisfaction with airbnb, which in turn, increases their intention to repurchase. similarly, lee and kim [11] reported that trust has a significant positive effect on user satisfaction and behavioral loyalty towards airbnb. topic 2 is related to the impact of airbnb on urban tourism, with the most frequent words being “city,” “urban,” and “tourism,” while the related frex words include “touristification” and “overtourism.” previous studies have explored the effects of airbnb on urban tourism, indicating that the platform has both positive and negative impacts on cities. on the positive side, airbnb has been shown to increase tourism and stimulate local economic development. however, it has also been associated with issues such as over tourism and changes in the character of urban neighborhoods. in particular, the phenomenon of “touristification” has been identified as a concern in some cities, where airbnb and other short term rental platforms have led to an increase in tourist oriented businesses and a decline in local culture [15,43]. topic 3 is concerned with the application of big data techniques in the context of airbnb for different purposes, such as algorithm development, prediction, and knowledge discovery [44,45]. big data has been recognized as a valuable resource for businesses, including those in the hospitality industry, as it provides access to large amounts of data that can be analyzed to identify patterns, trends, and insights. topic 4 is about the analysis of the airbnb website content for various purposes, ranging from understanding the platform's user behavior to developing strategies for successful listing management. the investigation into the airbnb website's content involves a comprehensive analysis of various data types, including textual data generated by customers' reviews, ratings, profile photos, and geolocation data. topic 5 explores the role of host guest interactions in shaping the guest experience and the overall success of hosting. these interactions occur before, during, and after the stay and have been linked to higher guest satisfaction, ratings, and bookings [15,23]. kim et al. [46] highlighted the importance of hosts being responsive and communicative during the pre stay stage to receive positive reviews and repeat bookings. however, effective host guest interactions are not always straightforward. they can be complicated by cultural differences, language barriers, and varying expectations, leading to misunderstandings [47]. topic 6 focuses on setting rules for using airbnb's lodging services, particularly those related to user safety. safety concerns have been found to be a significant factor in guests' decision making process when selecting an airbnb property and have been linked to guest satisfaction with their airbnb experience [22,48]. topic 7 focuses on research related to airbnb cybersecurity, specifically on detecting and analyzing web leaks on the airbnb platform. this is a niche but essential aspect of airbnb research, as it aims to identify potential vulnerabilities in the website and analyze past incidents of leaks, breaches, or other security incidents to ensure the safety and privacy of users. topic 8 is focused on investigating the different factors that motivate users to adopt the airbnb service. research has shown that there are several key factors that influence users’ decision to use airbnb over traditional hotels. so et al. [49] found that price value, enjoyment, and home benefits were important factors that led to the adoption of airbnb. other factors that have been found to motivate users to adopt airbnb include social influence, convenience, and the desire for unique and authentic experiences [15]. for example, users may be motivated to use airbnb because their friends or family members have recommended it, or because it offers a more convenient or flexible travel option than traditional hotels. additionally, users may be attracted to airbnb because it allows them to stay in unique and distinctive properties that offer a more authentic and immersive travel experience. topic 9 focuses on the governance of various sectors within the sharing economy, including transportation and hospitality, which have disrupted conventional business models. it is suggested that the sharing economy requires a new regulatory paradigm that balances the need to safeguard consumers with the need to promote innovation and competition [50]. topic 10 concerns the perceived risks associated with using airbnb as a consumer and how this impacts their willingness to use the platform. several studies have explored users' perceptions of the risks linked with airbnb, such as safety concerns [51], host reliability [52], and the experience of staying in a stranger's home [53]. another stream of studies explored the impact of perceived risks on consumer behavior and their motivation to choose airbnb over traditional hotel options [54,55]. topic 11 delves into the impact of airbnb on tourist destinations from various perspectives, such as economic growth, environmental sustainability, and tourism destination management. frequently used keywords that can help to define this topic include “tourism,” “tourist,” “destination,” and “impact.” the impact of airbnb on tourist destinations is a multifaceted issue that has been extensively researched. while the growth of airbnb and other home sharing platforms can provide new opportunities for local entrepreneurs and generate additional income for residents, concerns have been raised about the rise of short term rentals leading to increased housing costs and reducing the availability of long term housing, which can negatively impact the local economy [21]. additionally, the environmental impact of short term rentals, especially in areas with fragile ecosystems, has become a growing concern. topic 12 focuses on analyzing the p2p business model, which is a key characteristic of numerous sharing economy platforms. to illustrate and contrast the diverse p2p companies, airbnb is frequently used as a case study. topic 13 focuses on the comparison between airbnb and traditional hotels. given the similarities in the nature of service, researchers have compared airbnb with traditional hotels from various perspectives, such as accommodation pricing [7], spatial distribution [56], contribution to tourism [16], and revenue growth [57]. topic 14 focuses on studies related to the pricing of airbnb listings. these studies examine the various factors that influence the price of airbnb listings and explore pricing strategies that hosts can use. for example, location, property type, availability, and the host's experience level are identified as factors that significantly impact the price of airbnb listings [58]. in addition, factors such as listing type, room size, location, and seasonality also affect the price [59,60]. researchers have also explored various pricing strategies that hosts can use to maximize their earnings on airbnb. for instance, one study proposed a pricing model that considers profile characteristics [61], while another suggested a dynamic pricing approach that enables hosts to adjust their prices in response to changes in supply and demand [62]. topic 15 focuses on the use of advanced digital technologies and how they can improve organizational performance and address challenges, using airbnb as a case study. bolton et al. [63] highlighted the integration of emerging technologies like artificial intelligence and blockchain into airbnb's platform to enhance the guest experience and host management. topic 16 investigates the factors that influence the online purchasing behavior of users on the airbnb platform, with a focus on both host and property attributes. host attributes include factors such as reputation, credibility, responsiveness, and profile completeness [64,65], while property attributes refer to descriptions and the property's ability to meet guests' specific needs. topic 17 explores the impact of airbnb on the housing and rental markets. studies have shown that a 10% increase in airbnb listings in a city leads to a 0.39% increase in rents and a 0.64% increase in house prices [66], as well as having a significant and positive impact on local housing prices [19]. the legal and regulatory perspective of airbnb's impact on the housing market has also been discussed. huang et al. [67] highlighted the need for effective regulation of short term rentals to avoid the negative effects they can have on the housing market, while nieuwland and van melik [68] argue that local governments should implement policies that balance the economic benefits of short term rentals with the potential negative impacts on the availability and affordability of housing for local residents. regarding the impact of airbnb on rents, gurran and phibbs [21] found that the proliferation of airbnb has caused a significant surge in short term rentals, which may limit the availability of long term rental housing and increase rental costs. liang et al. [69] discovered that the expansion of airbnb has resulted in the displacement of long term renters and a reduction in the availability of cost effective rental housing, resulting in negative social and economic consequences in neighborhoods. topic 18 covers a wide range of user experience aspects, including customer service, amenities, and cleanliness. an interesting trend in recent user experience research on airbnb is the increasing use of online review data mining as a methodology. topic 19 examines the concept of value creation in the context of airbnb, with the word “value” appearing in both the highest probability and frx word lists. most studies exploring airbnb's value creation are from the perspective of collaborative consumption in the sharing economy, with different values being discovered based on users' subjective perceptions. tussyadiah and zach [70] suggested that airbnb hosts create value for guests by providing unique and personalized experiences, while guests create value for hosts by providing social interaction and supplemental income. additionally, researchers have identified various types of value that airbnb provides, including economic, social, and environmental value [7,71]. topic 20 focuses on the preferred services of airbnb users. preferred services refer to the features and amenities that hosts offer on the platform, which are most desirable to guests. understanding these preferences can help hosts tailor their offerings and improve the overall guest experience [72]. it is important to note that preferences may vary depending on the location, type of property, and guest demographics [16,73]. in addition, previous studies have also discussed service recovery. tussyadiah and pesonen [23] emphasized that service recovery is critical in determining user satisfaction with airbnb. users who have experienced a service failure but received a prompt and effective response from the company are more likely to have a positive view of the platform and recommend it to others. fig. 6 depicts the marginal effect of the journal field on the distribution of 20 topics, where the mean values of the estimated differences are represented by dots, and the 95% confidence intervals of the differences are shown as bars. the placement of the topics on the plot reflects their likelihood of appearing in either hospitality and tourism journals or non hospitality and tourism journals. specifically, topics located towards the left of the plot are more likely to appear in non hospitality and tourism journals, while those on the right are more likely to appear in hospitality and tourism journals. the findings reveal that certain topics, such as ‘trust in airbnb,’ ‘perceived risk,’ ‘host guest interactions,’ ‘value creation,’ and ‘airbnb user experience analysis’ are more frequently discussed in hospitality and tourism journals. these topics are primarily related to research on airbnb user behavior. on the other hand, topics that appear more frequently in non hospitality and tourism journals cover multiple research areas, including business analysis (e.g., ‘technological innovation’ and ‘business model analysis’), p2p platform management (e.g., ‘house rules,’ ‘governance of sharing economy platforms,’ and ‘airbnb cybersecurity’), and the impact of airbnb on various domains (e.g., ‘impacts on urban tourism’ and ‘impacts on housing prices and rents’). change in topic prevalence based on the journal field. note: the marginal effects depicted in this figure pertain to the change of expected proportions of topic prevalence based on the distinction between two journal fields: hospitality and tourism journals, and non hospitality and tourism journals. source: created by the authors. fig. 7 presents a map of the intercorrelations among the extracted topics. three topics, specifically ‘airbnb cybersecurity,’ ‘buying behavior,’ and ‘airbnb user experience analysis,’ are not included in the topic correlation map due to their weak correlation with other topics in the analysis. the thickness of the connecting lines represents the strength of the correlation between two topics, while the size of the label font indicates the relative proportion of the topic. the topics are grouped into five clusters, each with distinct characteristics. two clusters consist of only two topics: ‘technological innovation’ and ‘business model analysis,’ and ‘governance of sharing economy platforms’ and ‘house rules.’ these clusters explore the dynamics of the sharing economy and the regulatory mechanisms that govern it, highlighting the significance of sound business practices and innovation in the sharing economy. three topics in the cluster focus on airbnb's impact on different domains, including ‘impacts on urban tourism,’ ‘impacts on tourist destinations,’ and ‘impacts on housing prices and rents.’ these topics delve into the social and economic implications of airbnb's operations and underscore the importance of understanding the impacts of short term rentals on local communities. the cluster with five topics centers on ‘airbnb pricing,’ which highly correlates with ‘comparisons of airbnb and hotels’ and ‘airbnb website content analysis.’ these topics examine pricing strategies in the sharing economy, compare airbnb's pricing with traditional hotel pricing, and investigate the influence of airbnb's website content on consumer behavior. ‘value creation in airbnb’ and ‘perceived risk’ are the representative topics of the last cluster, which focuses on research on airbnb users. these topics investigate the factors that motivate consumers to use airbnb and the perceived risks associated with participating in the sharing economy. topic correlation map. note: the thickness of the connecting lines represents the strength or closeness of the connections between the topics, while the size of the text corresponds to the percentage representation of each topic. source: created by the authors. fig. 8 displaces the changing patterns of representative topics from different clusters, as presented in fig. 7, from the year 2015 2022. the proportional values were estimated using the “stm” package's estimateeffect function, which is widely used in topic modeling analysis. the results demonstrate that half of the ten representative topics showed an upward trend, indicating a sustained interest in these topics over the years. these topics include ‘trust in airbnb,’ ‘airbnb website content analysis,’ ‘perceived risk,’ ‘comparisons of airbnb and hotels,’ and ‘impacts on housing prices and rents’. in contrast, the remaining five topics experienced a decline in research interest over the same period. these topics include ‘impacts on urban tourism,’ ‘host guest interactions,’ ‘governance of sharing economy platforms,’ ‘business model analysis,’ and ‘value creation in airbnb.’ the changing trends in research interest reflect the evolving landscape of the sharing economy and the growing impact of airbnb on various sectors. the changing trend of each topic can be further explored to gain a more in depth understanding of the issues at hand. the evolving pattern of each topic extracted in this study is illustrated in fig. s2, which can supplement the content in fig. 8. annual trends of the selected topics. note: the annual trends depicted in this figure illustrate the temporal evolution of the selected topics between 2015 and 2022. source: created by the authors. through the analysis of the 20 topics extracted in this study, it is found that the major academic concerns regarding airbnb are related to its operational practices and impacts on various fields. concerning operational practices, the most relevant research topics include ‘trust in airbnb,’ ‘house rules,’ ‘airbnb pricing,’ and ‘value creation in airbnb.’ in depth investigations on these subjects provide airbnb with valuable insights for formulating effective property management policies, while also equipping hosts with practical strategies to optimize their p2p rental businesses. in the realm of airbnb users, six prominent research topics have emerged: ‘host guest interactions,’ ‘perceived risk,’ ‘service preferences of users,’ ‘buying behavior,’ ‘motivations of airbnb users,’ and ‘airbnb user experience analysis.’ among these topics, studies centered around user experience are the most prevalent, delving into diverse facets of airbnb users' lodging experiences. as airbnb's accommodation services evolve, users have increasingly diverse expectations, prompting research that explores different aspects of user needs. for instance, there are studies comparing the needs of different cross cultural airbnb users, expectations of users selecting different airbnb accommodation products, and analyses of changing trends in user preferences. as for airbnb's impacts, the related research topics include ‘impacts on urban tourism,’ ‘impacts on housing prices and rents,’ ‘impacts on tourist destinations,’ and ‘comparisons of airbnb and hotels.’ among the studies related to tourism, researchers have reported the positive and negative impacts of airbnb. previous studies have found that for well known tourist destinations, the boosting effect of airbnb on tourism has not been well received by local residents, as over tourism brought by airbnb has become an important factor of dissatisfaction for residents of these destinations. in contrast, for some developing countries, airbnb is widely recognized as a boost to tourism, and it is supported by government agencies in certain countries [73], as it not only promotes the development of the tourism economy but also alleviates the employment pressure of the local population to a certain extent. another area of research that has received a lot of attention from researchers is the impact of airbnb on the traditional hotel industry. by the very nature of the industry, the two types of accommodations are in direct competition with each other, but findings from previous studies are inconsistent. researchers have found that airbnb's impact on the traditional hotel industry is influenced by a very large number of factors, such as geography, hotel category, and location of the hotel, making it difficult to make direct inferences that airbnb has a direct impact on the traditional hotel industry. the remaining research topics include ‘data applications,’ ‘governance of sharing economy platforms,’ ‘airbnb cybersecurity,’ ‘technological innovation,’ and ‘business model analysis.’ the utilization of surveys as a research method has persisted as a popular approach among scholars investigating the airbnb phenomenon [53,55,64,66]. meanwhile, the application of regression analysis based on airbnb listing data has also garnered significant attention from researchers [58,59,62,69]. in order to obtain such data, scholars often depend on airdna [59,62,77], a reputable data analytics enterprise that charges for its services. on the other hand, insideairbnb (www.insideairbnb.com) is a freely available resource that many researchers employ to gather research data [12,45,58]. nevertheless, the reliability of the data from insideairbnb is a matter of concern [74]. alsudais [74] revealed two significant issues with the dataset's quality, which are likely due to systemic errors in the data collection process. in addition, the reproducibility of research outcomes may be compromised when comparing different iterations of the dataset. therefore, it is suggested that researchers must exercise caution when using open datasets and thoroughly assess the data's validity before drawing any conclusions [74]. research on airbnb has been published in both hospitality and tourism journals as well as non hospitality and tourism journals, and we have identified some differences in the emphasis of topics in these two types of journals. hospitality and tourism journals tend to prioritize topics such as “host guest interactions,’ ‘airbnb user experience analysis,’ and ‘airbnb pricing’. these topics are of significant interest to researchers in the hospitality and tourism field as they directly relate to the industry. conversely, non hospitality and tourism journals demonstrate a broader scope of interest regarding airbnb research. examples of such topics include ‘airbnb cybersecurity,’ ‘governance of sharing economy platforms,’ and ‘impacts on housing prices and rents.’ these areas of investigation reflect the interdisciplinary nature of the sharing economy phenomenon and its implications for various fields such as technology, law, and urban planning. while there is some overlap in the topics covered by these two types of journals, the differences reflect the disciplinary focus of the respective journals. the findings of this study suggest that there has been a shift in focus in airbnb related research over time. the topics that have shown significant growth in prevalence, such as ‘trust in airbnb,’ ‘airbnb user experience analysis’ and ‘airbnb pricing,’ indicate that there is increasing attention on issues related to user experience and pricing strategies on airbnb. among the studies related to airbnb user experience, we found that in addition to the increasing hotness of this research topic, there are significant changes in the methods of studying user experience. the main factor leading to the change in research methods is the continuous development of big data technology, which makes it possible to analyze a large scale of data efficiently. in airbnb user experience related research, many scholars applied text mining techniques to analyze user reviews posted on the platform to obtain important information, as these reviews often contain detailed descriptions of users' lodging experiences. to draw insight into these unstructured big data, many different text mining techniques have been applied, such as latent dirichlet allocation (lda) [45], latent semantic analysis (lsa) [46], and stm [75]. meanwhile, the significant declines in topics such as ‘business model analysis,’ ‘governance of sharing economy platforms,’ and ‘technological innovation’ suggest that research attention on the sharing economy and related technologies may be waning. the stability of the prevalence of ‘value creation in airbnb’ may suggest that this topic is well established and is of enduring interest to researchers. the findings also suggest that issues related to the impact of airbnb on local residential areas and tourist destinations continue to be significant areas of interest for researchers. this is consistent with the ongoing debates around the effects of airbnb on local housing markets, communities, and tourism [68,76]. the trends in the prevalence of different airbnb related research topics reflect the dynamic nature of the sharing economy and the evolving concerns and interests of researchers in this field. this study contributes to the existing literature on airbnb research by offering a comprehensive review of recent studies in the field. this study quantifies the notable increase in the publication of airbnb articles over the past seven years, indicating rapid growth in research interest in the platform. from the perspective of methodology, this study extends previous airbnb literature reviews by applying stm in the review process, effectively addressing the common limitations associated with traditional qualitative reviews. for instance, this study employed larger samples with a wider scope, thereby reducing potential literature selection biases. by expanding the scope, this study encompasses a diverse set of airbnb related topics, offering insights into various dimensions of the platform. specifically, this study extracts 20 airbnb related research topics from these documents, which contributes to a more nuanced understanding of the research landscape surrounding airbnb. in addition, the methodology employed in this study enables the acquisition of comprehensive insights from a substantial corpus of research papers in a prompt and efficient manner. this efficiency is particularly valuable in a rapidly evolving field like airbnb, where staying up to date with the latest research is crucial. therefore, the detailed steps for applying stm presented in this study serve as a useful reference for future literature reviews on airbnb. by utilizing the advantages of stm, this study further investigates how these research topics vary across hospitality and tourism journals as well as non hospitality and tourism journals by incorporating document metadata information. furthermore, this study contributes to the identification of underlying trends that change airbnb research priorities over time. for instance, the increasing prevalence of research topics such as ‘trust in airbnb’ and ‘impacts on housing prices and rents’ indicates a growing interest in the social and environmental effects of airbnb. this trend may prompt more research on the company's social responsibility and sustainability practices. conversely, the decreasing prevalence of topics such as ‘business model analysis’ and ‘technological innovation’ suggests that these areas have been extensively studied, urging future research to explore new avenues of inquiry. this study has implications not only for the academic literature on airbnb research but also for practitioners in the airbnb business and policymakers responsible for regulating the platform. the findings of this study can provide insights and guidance to those involved in the airbnb business regarding emerging trends in research and areas where future research could be focused. by keeping up with the latest research, practitioners can better understand the evolving expectations and demands of their customers and make informed decisions on how to optimize their business operations. furthermore, policymakers and regulatory bodies can benefit from the insights provided by this study to better understand the potential impact of sharing economy platforms like airbnb on their industries. with the increasing popularity of airbnb, concerns have been raised about its impact on rental housing markets, local communities, and traditional hotels. this study can inform policymakers about the potential risks and benefits associated with airbnb and provide recommendations for adapting regulations to ensure the safety and security of consumers and the maintenance of fair competition in the marketplace. policymakers can also use this study to evaluate the effectiveness of existing regulations and identify areas where changes may be necessary to ensure that the sharing economy operates in a socially responsible and sustainable manner. researchers have made considerable efforts to explore airbnb from various perspectives. however, the sheer volume and increasing diversity of airbnb studies present challenges in identifying major research topics and emerging trends through traditional qualitative analysis methods alone. this study fills the gap by providing a comprehensive overview of existing airbnb research, leveraging the methodological advantages offered by text mining techniques. by employing these techniques, this study identified two prominent academic concerns regarding airbnb, namely operational practices and impacts on various fields. concerning operational practices, the focus encompasses multiple facets of running a peer to peer accommodation business. this includes aspects such as pricing strategies, formulating comprehensive accommodation rules to ensure safety and security, as well as developing effective strategies for enhancing the overall user experience. moreover, the impacts of airbnb on urban tourism, housing prices and rents, tourist destinations, and the traditional hotel industry have been subjects of extensive investigation. this study sheds light on these impacts, presenting a nuanced understanding of the complex relationships between airbnb and these sectors. it is noteworthy that the research focus has evolved over time, with a shift from macro level analyses to micro level aspects. notably, there has been an increasing emphasis on understanding user experiences and pricing strategies, reflecting the growing significance of these factors in shaping the airbnb ecosystem. conversely, topics such as business model analysis and technological innovation have seen a decline in research attention. furthermore, this study reveals an interesting disparity between research articles published in non hospitality and tourism journals compared to those in hospitality and tourism journals. articles in non hospitality and tourism journals cover a more diverse range of topics, reflecting the interdisciplinary nature of airbnb research. this diversity suggests that scholars from various disciplines recognize the relevance and impact of airbnb beyond the boundaries of traditional hospitality and tourism research. while this study holds considerable implications, it is important to acknowledge its limitations when interpreting the results. the conclusions drawn are based on a specific selection of journals, and only abstracts were analyzed, potentially leading to an incomplete representation of the research papers’ content. despite the wide coverage of the scopus database, there is a possibility of overlooking relevant studies published in non indexed journals. the exclusion of studies published in non indexed journals raises the possibility of missing valuable insights that could have enriched the analysis. moreover, certain topics identified in the analysis may encompass sub topics that were not fully captured by the designated names of those topics, limiting the comprehensive understanding of the themes discussed in the full texts of the articles. the findings of this study provide valuable insights and suggest several directions for future research. previous studies on airbnb underscore the significance of understanding regional characteristics within the platform [14,18,45]. to advance this understanding, future research should conduct comparative studies that classify regions based on their distinct characteristics. for instance, comparisons between cities in developed and developing countries or among different developed countries would shed light on variations in airbnb dynamics. as the p2p accommodation industry grows more competitive, prioritizing customer experience is paramount for the sustainable development of the business [63]. to comprehend and cater to the diverse needs of various market segments on airbnb, researchers should adopt an interdisciplinary approach in exploring user preferences rather than relying solely on generalizations [73]. incorporating perspectives from fields like anthropology, sociology, and psychology can yield a more comprehensive understanding of users, unveiling nuanced insights. this knowledge can serve as a valuable reference for practitioners to create personalized and engaging customer experiences. furthermore, with the advent of technologies like machine learning and artificial intelligence, researchers can investigate the potential for airbnb to enhance user experiences through design and technological innovations. moreover, as the p2p accommodation market continues to mature, an increasing number of professional hosts are joining the platform [77]. however, there is currently limited understanding of user perceptions of professional management in airbnb accommodations. the question of whether a professionalized airbnb experience can lead to further business growth is worthy of exploration, as the distinction between a standardized airbnb and traditional hotels will further decrease. with sustainability becoming increasingly important in the sharing economy [78], researchers can investigate the role of airbnb in promoting sustainable and eco friendly tourism, as well as its impact on local communities and the environment. as covid 19 restrictions ease and tourism destinations reopen, it is urgent to address the challenges posed by airbnb. in this context, conducting studies on effective tourism destination management becomes crucial for policymakers, industry practitioners, and researchers. all authors listed have significantly contributed to the development and the writing of this article. this research is supported by the start up funds for scientific research of high level talents from ningbo university of finance and economics (grant no. 100064/1320229064). data will be made available on request. the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e17090. the following is the supplementary data to this article: this section collects any data citations, data availability statements, or supplementary materials included in this article. data will be made available on request. articles from heliyon are provided here courtesy of elsevier connect with nlm national library of medicine 8600 rockville pike bethesda, md 20894

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supported by room and board by kate sinclair when benjamin capitano moved into his dorm as a sophomore at the king’s college, a tiny liberal arts college in manhattan, he realized he’d been given a gift: a couch. so he listed his bed on airbnb for $99. “i’ve always had a knack for hospitality,” mr. capitano said. “i got us sheets and the little mini soaps and basically set up my dorm like a hotel.” at columbia university, new york university and the university of pennsylvania ($1,000 a night for a room for two during the pope’s philadelphia visit), students have been using airbnb to offset the cost of college. the listings tend to omit one fact: that the bed is in student housing. (potential guests, be sure to check pictures for telltale dorm furniture, and reviews: “it’s in a dorm…. i sort of felt creepy around all the kids,” said one guest at the school of visual arts.) while most housing contracts prohibit rentals, lately colleges have been reviewing their policies to ensure they explicitly mention airbnb. the king’s college had only a guest policy. mr. capitano said he was careful to follow the rules and submit the right forms to his resident adviser   “conveniently, i was the r.a.” more than 20 guests stayed, a bounty he split with his two roommates. but word got around. after a tense meeting with housing officials, restrictions were promptly added. mr. capitano was able to finish up the semester as r.a., but others have not fared as well. a student at emerson college in boston is facing a disciplinary hearing for renting his dorm room on airbnb.

february 4, 2016 want to spend a weekend in chicago? at a rate of $320 per night, plus taxes and fees, you could stay at “samson’s estate,” which, according to a listing on the popular rental website airbnb, offers wireless internet, air conditioning, two beds, and a half bath, shampoo included. the catch? it’s a dorm room at the university of chicago. or, at least, it was advertised that way, featuring an image of the university seal and the description: “a quiet dorm in the middle of uchicago’s campus in the max palevsky dorm.”