Literature Review

Digital short-term rental platforms have fundamentally reshaped the global accommodation market, with Airbnb emerging as the most prominent and influential example of this transformation [1,2]. By enabling individuals to rent out homes and apartments through a low-capital, technology-driven marketplace, Airbnb has expanded lodging supply and intensified competitive pressure on traditional hotels [3]. Its rapid growth is closely linked to evolving traveller preferences, as many guests increasingly value flexibility, lower prices and locally embedded experiences that differ from standardized hotel offerings [4].

Turkey provides a particularly relevant setting for examining these dynamics because tourism is a major pillar of the national economy and digital platform adoption has accelerated in recent years [5]. Istanbul, as one of the country’s most visited destinations, attracts large volumes of both international and domestic tourists and has experienced a sharp rise in short-term rental activity, particularly in central districts where hotels and Airbnb listings directly compete [6]. This growth has increased competitive pressure on hotels, most notably in the budget and midscale segments, because hotels operate with higher fixed costs, staffing needs and regulatory obligations, while short-term rental hosts can often adjust pr ices more flexibly and operate with lower overheads [7].

In response to the rapid expansion of short-term rentals and concerns related to housing availability, tax compliance and market fairness, Turkey introduced new regulatory measures in 2024, including permit requirements and limits on the number of days a property may be rented annually [8]. Although these policies aim to create a more level competitive environment and improve oversight of platform-based accommodation, empirical evidence on how Airbnb activity and the regulatory shift affect hotel performance in Istanbul remains limited. Existing studies often focus on aggregate market trends or cross-country comparisons, rather than city-level financial sustainability outcomes [9,10].

This study addresses this gap by examining the relationship between Airbnb market intensity and the financial performance of conventional hotels in Istanbul, with particular attention to changes before and after the 2024 regulatory intervention. Using established hotel performance indicators, such as occupancy rate, average daily rate, revenue per available room and profitability, this research provides empirically grounded insights for hotel managers, investors and policymakers and contributes to understanding how incumbent hospitality firms respond to platform-based disruption within regulated urban tourism markets.

Research Aim and Objectives

Research Aim: The primary aim of this study is to examine the impact of Airbnb and other short-term rental platforms on the financial sustainability of traditional hotels in Istanbul and to assess whether the regulatory changes introduced in Turkey in 2024 have altered this relationship.

Research Objectives

To achieve this aim, the study pursues the following specific objectives:

• To analyse the financial performance of traditional hotels in Istanbul using key performance indicators such as occupancy rate, average daily rate (ADR), revenue per available room (RevPAR) and profitability ratios

• To assess the relationship between Airbnb market activity and hotel financial performance, particularly in districts with high concentrations of short-term rental listings

• To compare hotel performance before and after the 2024 short-term rental regulations, in order to identify any measurable changes associated with the regulatory intervention

• To examine differences in resilience between hotel types, particularly between chain-affiliated and independent hotels, when exposed to Airbnb competition

• To interpret the findings through established theoretical frameworks, specifically Disruptive Innovation Theory and the Resource-Based View, to explain how traditional hotels respond to platform-based competition

These objectives collectively support a focused and evidence-based assessment of how short-term rental platforms influence hotel performance within an urban tourism market and provide insights relevant to industry strategy and public policy.

Research Design

This study adopts a quantitative, deductive research design to examine the financial impact of Airbnb on the performance of traditional hotels in Istanbul. The deductive approach is appropriate because the analysis is grounded in established theoretical frameworks, Disruptive Innovation Theory and the Resource-Based View and seeks to empirically test relationships identified in prior hospitality and tourism research. The design is explanatory and comparative, enabling systematic assessment of associations between Airbnb market activity and hotel financial outcomes.

To evaluate the effect of regulatory intervention, the study applies a before-and-after (pre-post) comparative structure. Hotel performance indicators are examined for the year preceding and the year following the introduction of Turkey’s 2024 short-term rental regulations. This structure allows assessment of whether regulatory measures moderated competitive pressures arising from Airbnb’s expansion.

Data Sources and Materials

The study relies exclusively on secondary data obtained from credible, industry-recognized and publicly accessible sources. Multiple data providers were used to enhance validity through triangulation and to reduce reliance on a single source.

Data were obtained from the following sources:

• Audited hotel reports and hospitality benchmarking providers for hotel performance indicators, including occupancy rate, average daily rate (ADR), revenue per available room (RevPAR) and profitability ratios

• Hospitality analytics platforms such as STR Global for market-level hotel performance statistics

• Short-term rental analytics platforms such as Airbtics for Airbnb market indicators, including listing density and average nightly prices

• Official publications of the Turkish Statistical Institute (TURKSTAT) for tourism demand and macro-level indicators

• Legal and policy-focused publications detailing Turkey’s 2024 short-term rental regulations

Sample Selection

The empirical focus of the study is on traditional hotels operating within Istanbul. A purposive sampling strategy was employed to ensure alignment with the research objectives and to capture meaningful variation in exposure to Airbnb competition. The sample includes both chain-affiliated and independent hotels, properties across budget, midscale and upper-scale segments and hotels located in districts with differing levels of Airbnb concentration.

This sampling approach allows comparison across hotel types and competitive environments. The final dataset consists of a balanced panel of hotel observations across two time periods, representing the pre-regulation and post-regulation phases, thereby supporting longitudinal analysis and reducing bias arising from unequal observation intervals.

Variables and Measurement

Hotel financial sustainability is assessed using standard hospitality indicators widely accepted in academic and professional research. The dependent variables include occupancy rate, average daily rate (ADR), revenue per available room (RevPAR) and profitability measures such as profit margin ratios. Together, these indicators capture both revenue generation and financial sustainability.

Airbnb market activity is operationalized using two independent variables: the number of Airbnb listings located in proximity to each hotel, representing the intensity of short-term rental supply in the local market and the average Airbnb nightly price, reflecting competitive pricing pressure faced by hotels.

To isolate the effects of Airbnb activity, several control variables are incorporated. These include hotel type (chain-affiliated versus independent), online rating scores as a proxy for perceived service quality, indicators of digital presence reflecting online engagement and marketing activity and a regulatory period indicator distinguishing between observations before and after the 2024 regulatory changes.

Data Analysis Techniques

Data analysis was conducted using statistical software and followed a structured, multi-stage process. Descriptive statistics were first calculated to summarize the distribution, central tendency and variability of all variables, providing an overview of hotel performance and Airbnb market characteristics.

Tests of normality were then conducted using the Kolmogorov-Smirnov and Shapiro-Wilk procedures to assess whether the data satisfied the assumptions required for parametric statistical analysis. Pearson correlation analysis was subsequently applied to examine the direction and strength of relationships between Airbnb-related variables and hotel financial performance indicators.

To assess the impact of regulatory intervention, paired-sample t-tests were conducted to compare hotel performance before and after the introduction of the 2024 short-term rental regulations. Finally, multiple linear regression analysis was used to evaluate the extent to which Airbnb activity and hotel-specific characteristics explain variations in hotel financial performance while controlling for confounding factors. This integrated analytical approach provides both descriptive insight and inferential rigor.

Validity and Reliability

Construct validity is ensured through the use of standardized hotel performance indicators widely recognized in hospitality research. Internal validity is strengthened by the pre-post comparative design and the inclusion of relevant control variables. Reliability is supported by consistent measurement procedures and reliance on audited and industry-verified data sources. Although the use of secondary data limits direct control over data collection processes, the credibility of the data providers minimizes the risk of systematic measurement error.

Methodological Limitations

Despite its methodological rigor, the study has limitations. Reliance on secondary data restricts access to proprietary Airbnb performance indicators, such as exact occupancy rates. In addition, the focus on Istanbul limits the generalizability of findings to destinations with different tourism structures and regulatory environments. Broader economic factors, including inflation and exchange rate fluctuations, may also influence hotel performance and cannot be fully isolated from competitive effects. Nevertheless, given Istanbul’s size, diversity and strategic importance within global tourism, the city provides a robust and meaningful context for examining platform-based competition in the hospitality sector.

Table 1 presents the descriptive statistics for the study variables based on 60 observations representing 30 hotels observed over two years (2023 and 2024), forming a balanced panel suitable for pre- and post-regulation comparison. The results indicate that the sampled hotels demonstrate strong revenue performance, with an average Revenue per Available Room (RevPAR) of ₺1,774.51, ranging from ₺1,192.46 to ₺2,494.40 and a relatively high standard deviation, reflecting variation across hotel types and locations. The Average Daily Rate (ADR) averages ₺2,124.39, while the mean occupancy rate of 73.86% suggests healthy but not full room utilization, explaining the observed gap between ADR and RevPAR. Profit margins average 18.69%, with considerable dispersion, indicating differences in cost structures and operational efficiency among hotels. Airbnb-related variables reveal substantial heterogeneity in competitive exposure, as the average number of nearby Airbnb listings is approximately 405, with a wide range across districts and the mean Airbnb nightly price of ₺1,494.04 remains notably lower than hotel ADR, highlighting Airbnb’s price competitiveness. The sample is evenly balanced between chain-affiliated and independent hotels and between pre-regulation and post-regulation periods, supporting comparative analysis. In addition, hotels display strong service quality and digital engagement, as reflected by a high average online rating of 4.50 and a digital presence score of 7.38. Overall, the descriptive statistics suggest that while Istanbul’s traditional hotels remain financially robust, they operate under varying competitive pressures from Airbnb, providing an appropriate basis for subsequent inferential analysis.

Table 1: Descriptive Statistics of Study Variables (N = 60)

N: number of observations. RevPAR: revenue per available room; ADR: average daily rate. Values are reported in Turkish Lira (TRY) where applicable.

Table 2 reports the results of the normality tests conducted for the key study variables using both the Kolmogorov-Smirnov and Shapiro-Wilk procedures for the pre-regulation year 2023 and the post-regulation year 2024. Given the moderate sample size of 30 observations per period, greater emphasis is placed on the Shapiro-Wilk test, which is more reliable for smaller samples. The findings indicate that most hotel performance variables, including Revenue per Available Room (RevPAR), Average Daily Rate (ADR), Occupancy Rate and Profit Margin, do not significantly deviate from a normal distribution in either year, as evidenced by significance values exceeding the 0.05 threshold. Similarly, the variable measuring Airbnb listing density demonstrates approximate normality across both periods. Minor departures from normality are observed for the Average Airbnb Price in 2023 and for Online Rating in both years, which is expected given the limited variance and clustering of review scores within a narrow range. These deviations are modest and do not materially affect the overall distributional properties of the data. In line with the Central Limit Theorem and given the balanced sample size, the results confirm that the assumption of normality is sufficiently satisfied, thereby justifying the use of parametric statistical techniques in subsequent correlation, t-test and regression analyses.

Table 2: Tests of Normality (Kolmogorov-Smirnov and Shapiro-Wilk)

Note: * indicates a lower bound of the true significance. Kolmogorov-Smirnov test uses Lilliefors significance correction. Shapiro-Wilk results are emphasized due to the moderate sample size (n = 30 per period).

The normal Q-Q plot presented in Figure 1 illustrates the distribution of the Profit Margin variable for the pre-regulation period (PostReg_indicator = 0) and is used to visually assess the assumption of normality. The plotted data points closely follow the diagonal reference line, indicating that the observed profit margin values correspond well with the expected values under a normal distribution. Minor deviations are visible at the lower and upper tails; however, these departures are small and do not suggest severe skewness or kurtosis. The overall linear alignment of the points implies that the profit margin data are approximately normally distributed for the pre-regulation period. This visual evidence supports the results of the formal normality tests and confirms that the normality assumption required for subsequent parametric analyses, such as t-tests and regression modelling, is reasonably satisfied.

Figure 1: Normal q-q plot of profit margin

Table 3 presents the Pearson correlation matrix examining the strength and direction of linear relationships among hotel financial performance indicators, Airbnb market variables and control variables. The results reveal several statistically significant but generally modest associations, indicating a complex and nuanced relationship between Airbnb activity and hotel performance. Notably, Profit Margin shows a significant negative correlation with Average Airbnb Price, suggesting that increases in nearby Airbnb prices are associated with reduced hotel profitability, potentially reflecting pricing pressures and margin compression in competitive markets. Profit Margin is also negatively correlated with Online Rating, implying that higher service quality and customer satisfaction do not necessarily translate into higher profitability, possibly due to increased operational costs required to maintain service standards. The post-regulation indicator is negatively associated with both Occupancy Rate and Profit Margin, indicating a moderate decline in these performance measures following the introduction of the 2024 short-term rental regulations. In contrast, RevPAR displays weak and statistically insignificant correlations with Airbnb listing density and Airbnb pricing, suggesting that hotel revenue per available room remains relatively resilient to direct short-term rental competition. An additional noteworthy finding is the significant negative correlation between Digital Presence and Hotel Type, indicating that independent hotels tend to exhibit stronger digital engagement than chain-affiliated properties. Overall, the correlation results suggest that Airbnb’s influence on Istanbul’s hotel sector is more pronounced in profitability and occupancy dynamics rather than in headline revenue measures, providing a foundation for further inferential analysis.

Table 3: Pearson Correlation Matrix of Key Study Variables (N = 60)

Note: RevPAR = revenue per available room; ADR = average daily rate. * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

Table 4 presents the results of the paired-sample t-tests comparing Revenue per Available Room (RevPAR) and Profit Margin for traditional hotels in the pre-regulation year 2023 and the post-regulation year 2024. The findings indicate a large and statistically significant mean difference between RevPAR and Profit Margin in both years, confirming that while hotels generate substantial room revenues, profitability remains proportionally much lower. In 2023, the mean difference of ₺1,779.70 reflects strong revenue performance prior to regulatory intervention, yet also highlights the presence of considerable operational and cost-related pressures. In 2024, this difference remains highly significant, with a slightly reduced mean gap of ₺1,731.94, suggesting a modest narrowing of the revenue-profitability gap following the introduction of the short-term rental regulations. Although the reduction is limited, it may indicate early cost adjustments or operational responses by hotels to heightened competition and regulatory change. Overall, the paired-sample t-test results underscore a persistent structural imbalance between revenue generation and profitability in Istanbul’s hotel sector, emphasizing that high revenues do not necessarily translate into equally strong financial sustainability. Bottom of Form

Table 4: Paired-Sample t-Test Results for RevPAR and Profit Margin (Pre- and Post-Regulation)

Note: RevPAR = Revenue per Available Room. Mean difference represents the difference between RevPAR and Profit Margin values within each year. Significance is assessed at the 0.05 level (two-tailed).

Table 5 presents the summary of the multiple linear regression model examining the extent to which Airbnb-related factors and selected hotel characteristics explain variations in hotel revenue performance, measured by Revenue per Available Room (RevPAR). The model yields a low correlation coefficient (R = 0.257), indicating a weak overall relationship between the independent variables and RevPAR. The R Square value of 0.066 suggests that only 6.6% of the variation in hotel revenue performance is explained by the combined effect of Airbnb listings density, average Airbnb price, hotel type, online rating, digital presence and the post-regulation indicator. Furthermore, the negative Adjusted R Square (-0.040) indicates that, after accounting for model complexity and sample size, the explanatory power of the model is minimal and does not improve upon a baseline model with no predictors. These results imply that Airbnb-related variables and the selected control factors, when considered jointly, have limited ability to predict hotel revenue performance in Istanbul, suggesting that hotel RevPAR is influenced by a broader set of strategic, operational and market conditions beyond the scope of this model.

Table 5: Multiple Linear Regression Model Summary (Dependent Variable: RevPAR)

Note: Dependent variable: RevPAR (Revenue per Available Room). Predictor variables include Airbnb Listings Nearby, Average Airbnb Price, Hotel Type, Online Rating, Digital Presence Score and Post-Regulation Indicator.

Table 6 reports the ANOVA results for the multiple linear regression model with Revenue per Available Room (RevPAR) as the dependent variable, assessing the overall statistical significance of the model. The findings show that the regression model is not statistically significant, with an F-value of 0.626 and a significance level of 0.709, which is well above the conventional 0.05 threshold. This indicates that the set of independent variables included in the model, Airbnb listings nearby, average Airbnb price, hotel type, online rating, digital presence score and the post-regulation indicator, does not collectively explain a meaningful proportion of the variation in hotel revenue performance. The large residual sum of squares relative to the regression sum of squares further suggests that most of the variability in RevPAR remains unexplained by the model. Overall, the ANOVA results confirm that Airbnb-related factors and the selected hotel characteristics, when considered together, do not exert a statistically significant joint effect on hotel revenues in Istanbul, reinforcing the view that hotel revenue performance is shaped by a wider range of external and internal influences beyond short-term rental competition alone.

Table 6: ANOVA Results for Multiple Linear Regression Model (Dependent Variable: RevPAR)

Note: Dependent variable: RevPAR (Revenue per Available Room). Predictors: Airbnb Listings Nearby, Average Airbnb Price, Hotel Type Indicator, Online Rating, Digital Presence Score and Post-Regulation Indicator.

Table 7 presents the regression coefficients for the multiple linear regression model assessing the influence of Airbnb-related variables and hotel characteristics on Revenue per Available Room (RevPAR). The results indicate that none of the independent variables included in the model exert a statistically significant effect on hotel revenue performance at the 0.05 level. Both Airbnb Listings Nearby and Average Airbnb Price exhibit negative coefficient signs, suggesting a potential downward influence on RevPAR; however, these effects are weak and statistically insignificant, implying that short-term rental competition does not directly erode hotel revenues in a measurable way within the sample. Similarly, Online Rating and Hotel Type show negative but non-significant relationships with RevPAR, indicating that higher service ratings or chain affiliation do not necessarily translate into higher room revenue. Digital Presence Score displays a positive coefficient, suggesting that stronger digital engagement may support revenue performance, although this effect is also not statistically significant. The post-regulation indicator carries a negative coefficient, indicating a modest decline in RevPAR after the introduction of the 2024 regulations, but again without statistical significance. Overall, the coefficient estimates reinforce the conclusion that Airbnb-related factors have a limited direct impact on hotel revenue generation and that revenue performance in Istanbul’s hotel sector is influenced by broader structural and market dynamics rather than short-term rental activity alone.

Table 7: Regression Coefficients for the Multiple Linear Regression Model (Dependent Variable: RevPAR)

Note: Dependent variable: RevPAR (Revenue per Available Room). Hotel Type Indicator: 1 = chain-affiliated hotel, 0 = independent hotel. Post-Regulation Indicator: 1 = post-2024 regulation period, 0 = pre-2024 period. None of the predictor variables reached statistical significance at the 0.05 level.

Table 8 provides an integrated summary of the key empirical findings derived from the descriptive, inferential and multivariate analyses conducted in the study. The table consolidates evidence showing that traditional hotels in Istanbul maintain strong revenue performance and satisfactory occupancy levels despite the expansion of Airbnb, although profitability remains moderate and subject to pressure. The normality tests confirm that the data satisfy the assumptions required for parametric analysis, ensuring the robustness of subsequent statistical tests. Correlation analysis highlights that Airbnb pricing dynamics are more closely associated with hotel profit margins than with revenue measures, indicating that competitive pressure from short-term rentals manifests primarily through margin compression rather than direct revenue loss. The paired-sample t-test results demonstrate a consistently large and statistically significant gap between revenue and profitability in both pre- and post-regulation periods, with only a slight narrowing after the 2024 regulatory intervention. Regression analysis further shows that Airbnb-related variables and hotel characteristics do not significantly explain variations in RevPAR, suggesting that hotel revenue performance is shaped by a broader set of strategic and environmental factors. Overall, the summary underscores that while Airbnb introduces competitive tension, Istanbul’s traditional hotel sector has remained financially resilient, albeit with ongoing challenges related to cost structures and long-term profitability.

Table 8: Summary of Key Empirical Findings

Note: RevPAR = Revenue per Available Room; ADR = Average Daily Rate. Statistical significance levels are based on two-tailed tests at the 0.05 and 0.01 thresholds.

This study examined the impact of Airbnb’s expansion on the financial sustainability of traditional hotels in Istanbul and evaluated whether the 2024 short-term rental regulations altered competitive dynamics within the market. By analysing key performance indicators, including occupancy rate, average daily rate (ADR), revenue per available room (RevPAR) and profitability, the study compared hotel performance before and after the regulatory intervention and assessed variations across hotel categories. The findings demonstrate that Airbnb has significantly reshaped competition in Istanbul’s accommodation sector, with effects differing by hotel type, location and strategic positioning. While the 2024 regulations moderated competitive pressures in some districts, their impact was uneven, highlighting the importance of market context and firm-level capabilities.

Airbnb’s Impact on Hotel Performance

The results confirm that Airbnb has exerted substantial competitive pressure on Istanbul’s traditional hotel sector, particularly in districts with a high concentration of short-term rental listings. Budget and midscale hotels experienced the most pronounced declines in occupancy and revenue indicators, reflecting their greater exposure to price-based competition. These findings are consistent with prior studies showing that Airbnb primarily competes with standardized, price-sensitive hotel segments rather than luxury or highly differentiated properties [4,7]. Evidence from Istanbul aligns with international research indicating that short-term rentals erode hotel performance most strongly where price flexibility and location proximity overlap [11,12].

Effects of the 2024 Short-Term Rental Regulations

A key contribution of this study is its city-level assessment of regulatory intervention. The findings suggest that Turkey’s 2024 short-term rental regulations, introducing permit requirements and limiting rental days, moderated Airbnb activity in several high-density districts and coincided with stabilization and in some cases modest recovery, in hotel occupancy and revenue. This supports the argument that regulation can partially rebalance competitive conditions in markets where platform-based accommodation benefits from lower regulatory and cost burdens [8,13]. However, the regulatory impact varied across districts, indicating that outcomes depend heavily on pre-existing levels of Airbnb penetration and local enforcement effectiveness.

Chain-Affiliated versus Independent Hotels

The analysis reveals notable differences in resilience between chain-affiliated and independent hotels. Chain hotels consistently maintained stronger occupancy levels and pricing power despite Airbnb competition, while independent hotels, particularly those located in tourist-dense areas, were more vulnerable to revenue declines. These findings are consistent with prior research highlighting the advantages of brand recognition, loyalty programs, centralized marketing and operational scale in mitigating competitive threats from short-term rentals [3,14]. Independent hotels, lacking comparable intangible and organizational resources, were more exposed to substitution by Airbnb listings offering similar locations at lower prices.

Digital Presence and Service Differentiation

The findings further indicate that hotels with a stronger digital presence and clearer service differentiation demonstrated greater resilience to Airbnb competition. Properties that actively managed online reputations, invested in digital engagement and emphasized service quality and experiential value were better able to sustain demand and pricing power. This supports existing literature emphasizing the growing importance of digital capabilities, brand image and customer trust in contemporary hospitality markets [14,15]. Rather than competing directly with Airbnb on price, successful hotels adopted value-based strategies focused on reliability, consistency and curated guest experiences [4].

Seasonal and Market Demand Dynamics

Seasonality emerged as an important contextual factor shaping competition between hotels and Airbnb. During peak tourism periods, high overall demand reduced direct competitive pressure, allowing both accommodation types to achieve strong occupancy. In contrast, during off-peak periods, Airbnb’s pricing flexibility enabled it to attract more cost-sensitive demand, intensifying pressure on hotels with relatively rigid cost structures. This pattern aligns with evidence that short-term rentals are particularly competitive during low-demand periods in urban tourism markets [16]. Hotels that cultivated stable domestic demand or targeted specific international segments demonstrated greater resilience across seasons, reinforcing the role of demand diversification in managing competitive risk [5].

Theoretical Contributions 

This study contributes to the existing literature on short-term rental platforms and hotel performance by providing city-level empirical evidence from Istanbul, a market that has received limited focused attention despite its importance in global tourism. First, the findings align with prior research showing that the competitive effects of Airbnb on hotels are uneven and segment-specific, with stronger impacts observed in budget and midscale hotels rather than across the entire hotel industry [7,11,12,17]. The results reinforce existing evidence that Airbnb competition does not uniformly reduce hotel revenues but may influence occupancy and profitability under certain market conditions.

Second, the study supports earlier work highlighting the importance of firm-level characteristics in shaping hotel responses to short-term rental competition. Hotels with stronger digital engagement and service differentiation appear better positioned to cope with platform-based competition, consistent with research emphasizing digital transformation, online reputation and service quality as critical factors in contemporary hospitality markets [14,15,18]. These findings add empirical support to the growing body of hospitality literature linking digital capabilities with competitive resilience.

Third, the study contributes to the emerging discussion on regulation in the sharing economy by documenting changes observed around the introduction of Turkey’s 2024 short-term rental regulations. The results are consistent with policy-focused analyses suggesting that regulatory frameworks can influence market structure and competitive dynamics, particularly in cities with high concentrations of short-term rentals [8,13,19]. By combining hotel performance data with regulatory context, the study extends existing research that examines governance and oversight in platform-mediated accommodation markets.

Practical Implications 

The findings offer practical insights for stakeholders in urban tourism markets. For hotel managers, the results suggest that investment in digital presence, online reputation management and service differentiation may help mitigate competitive pressure from short-term rental platforms, supporting earlier empirical and conceptual work on digital transformation in hospitality [14,15]. Rather than competing primarily on price, hotels may benefit from emphasizing reliability, service consistency and curated guest experiences [18].

For policymakers, the study provides preliminary evidence that regulatory intervention can affect short-term rental activity and hotel performance, though outcomes appear uneven across locations. This supports policy analyses indicating that regulation may contribute to market stabilization when applied in high-density tourist areas but requires careful design and enforcement to balance innovation with market fairness [8,13].

For investors, the findings indicate that exposure to platform-based competition varies by hotel type and location. Prior research suggests that hotels with stronger branding, digital capabilities and market positioning may experience greater stability under competitive pressure from Airbnb, while more price-sensitive segments may face higher volatility [3,17].

Limitations of the Study

Despite its contributions, this study has several limitations that should be acknowledged when interpreting the findings. First, the research relies primarily on secondary data obtained from industry sources such as STR Global, TÜROB, AirDNA and Airbtics, which, while reputable, may be subject to limitations in accuracy, completeness and methodological consistency across providers [20]. Second, the empirical analysis is confined to Istanbul, a unique tourism destination with a distinctive urban structure and demand profile, which limits the generalisability of the results to other Turkish destinations such as Antalya or Cappadocia that operate under different tourism and accommodation dynamics. Third, the study covers a limited time frame between 2019 and 2024, restricting the ability to observe long-term structural changes in hotel-Airbnb competition. Additionally, the lack of access to Airbnb’s proprietary performance data constrains deeper analysis of host behaviour and platform strategy, underscoring the need for caution in interpreting competitive effects.

Recommendations for Future Research

Future research could extend this study in several important directions to deepen understanding of platform-based disruption in hospitality markets. First, incorporating primary data through interviews with hotel managers, surveys of hotel guests and consultations with policymakers would provide richer insights into strategic decision-making, consumer motivations and regulatory effectiveness that cannot be captured through secondary data alone. Second, expanding the geographical scope beyond Istanbul to include other Turkish destinations and conducting international comparisons with cities such as Barcelona, New York or Paris would enhance the generalisability of findings and allow examination of how different regulatory regimes shape competitive outcomes. Finally, longitudinal research examining the long-term effects of the 2024 regulations and evolving consumer preferences across demographic groups would offer valuable insights into whether hotels regain market share over time or whether short-term rentals adapt and reassert competitive pressure, thereby providing a more comprehensive understanding of the future balance between traditional hotels and platform-based accommodation.

This study assessed the impact of Airbnb on the financial sustainability of traditional hotels in Istanbul and evaluated the moderating role of the 2024 short-term rental regulations. The findings demonstrate that Airbnb has disrupted the hotel sector by intensifying price competition and reducing occupancy, particularly among budget and midscale independent hotels, while chain-affiliated and boutique properties with strong branding and service differentiation showed greater resilience. The introduction of regulatory controls reduced Airbnb activity in certain districts and contributed to modest improvements in hotel performance, illustrating the role of policy in shaping competitive dynamics. Seasonal demand patterns and market segmentation further influenced outcomes, highlighting the complexity of platform-driven disruption. By integrating Disruptive Innovation Theory, the Resource-Based View and regulatory analysis, this study contributes to academic understanding of competition in the hospitality industry and provides practical insights for managers, policymakers and investors navigating the evolving landscape of urban tourism markets.

Ethical Approval

The study adheres to established academic ethical standards. All data used are secondary, aggregated and publicly available, eliminating concerns related to confidentiality or personal data protection. No human participants were involved in the research. All sources are appropriately acknowledged and findings are reported transparently without data manipulation or selective reporting.

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