1# RECORD fb8eee688ea40496609ebe82ad7180db4acffe2c2# TITLE: The impact of short-term rental activity on house prices: evidence from coastal Virginia3# YEAR: 2024.0 VENUE: The annals of regional science DOI: 10.1007/s00168-024-01328-44# AUTHORS: Navid Safari; Lei Zhang; Timothy Komarek5# PAGES: 33 CANDIDATE TABLE PAGES (1-based): [12, 14, 16, 17, 18, 25, 26, 27, 28, 29, 30, 31]6# ABSTRACT: 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.789===== PAGE 1 (score 5, numeric-paren 0, table-words 0, var-words 5) =====1011Vol.:(0123456789)12The Annals of Regional Science (2025) 74:613https://doi.org/10.1007/s00168-024-01328-414ORIGINAL PAPER15The impact of short‑term rental activity on house prices: 16evidence from coastal Virginia17Navid Safari1 · Lei Zhang2 · Timothy M. Komarek318Received: 7 December 2022 / Accepted: 5 December 2024 / Published online: 31 December 2024 19© The Author(s) 202420Abstract21Short-term rentals such as Airbnb have grown substantially in recent years. In this 22study, we investigated the effect of Airbnb rentals on neighboring house prices in 23Hampton Roads, Virginia. We focus on three different measures of Airbnb activity 24to obtain more robust results. Our measures include active Airbnb listings, active 25listings with at least one reservation, and the sum of reservations over three differ-26ent periods. Consistent with previous literature, our main results show that prior 27Airbnb rental activity has a positive impact on the surrounding housing sales prices. 28Furthermore, the localized impact diminishes over geographic space (i.e., distances 29greater than 500 m). We also examine how Airbnb rental activity impacts house 30prices across the price distribution of home sales, using quantile regression. We find 31that the effect is strongest for higher-priced homes, while lower-priced houses are 32less impacted by Airbnb rentals.33Keywords Short-term rental · Airbnb · House price · Hedonic model · Quantile 34regression35 * Lei Zhang 36 37l2zhang@odu.edu38 39Navid Safari 40 41navid.safari@gcsu.edu42 43Timothy M. Komarek 44 45Tim.Komarek@colostate.edu461 47J. Whitney Bunting College of Business and Technology, Georgia College & State University, 48Milledgeville, GA, USA492 50Department of Finance, Strome College of Business, Old Dominion University, Norfolk, VA, 51USA523 53Department of Economics, Colorado State University, Fort Collins, CO, USA545556===== PAGE 12 (score 15, numeric-paren 0, table-words 4, var-words 3) =====5758 59N. Safari et al.606 61Page 12 of 3362included in the regression analyses as a hedonic attribute. We have three sets of 63models with different assumptions for distance and time to explore the impact of 64Airbnb listings on housing prices.65Table 2 Description and summary statistics for Airbnb densities (independent variables) and ln(price) 66(dependent variable). Airbnb properties and housing sales in Hampton Roads, Virginia (January 2015–67December 2019)68For each house sold, we drew three concentric rings with different radii: Ring 1, 0–300 m; Ring 2, 300–69500 m; and Ring 3, 500–1000 m70Phase 1 includes the month of sale and the three months before that. For example, if a house is sold in 71August; thus, Phase 1 includes May, June, July, and August. Phase 2 includes the fourth month before 72the sale and three months before that. Suppose a house is sold in August; thus, Phase 2 includes Janu-73ary, February, March, and April. Phase 3 includes the eighth month before the sale and three the months 74before that. Suppose that a house is sold in August; thus, Phase 2 includes September, October, Novem-75ber, and December76The mean of the inflation-adjusted house sale price (unlogged) is $242,111.9077Variable78Description79Mean Std. Dev Min80Max81Independent variables82Measure 1: “Active” Airbnb83Airbnb_Phase1 _Ring1 Number of Airbnb in phase1 and ring 1840.803854.158608713888Airbnb_Phase1 _Ring2 Number of Airbnb in phase1 and ring 2891.028904.2299109214393Airbnb_Phase1 _Ring3 Number of Airbnb in phase1 and ring 3943.797 12.0369509626797Airbnb_Phase2 _Ring1 Number of Airbnb in phase2 and ring 1980.645993.5371000101136102Airbnb_Phase3 _Ring1 Number of Airbnb in phase3 and ring 11030.6691043.7271050106144107Measure 2: “Positive Occupancy Rate”108Airbnb_Phase1 _Ring1 Number of Airbnb in phase1 and ring 11090.6781103.5881110112138113Airbnb_Phase1 _Ring2 Number of Airbnb in phase1 and ring 21140.8751153.7371160117130118Airbnb_Phase1 _Ring3 Number of Airbnb in phase1 and ring 31193.211 10.661200121255122Airbnb_Phase2 _Ring1 Number of Airbnb in phase2 and ring 11230.5161242.9231250126132127Airbnb_Phase3 _Ring1 Number of Airbnb in phase3 and ring 11280.5631293.2091300131133132Measure 3: Sum of Reservations133Airbnb_Phase1 _Ring1 Number of reservations in hundreds in phase1 134and ring 11350.0801360.481137013831.13139Airbnb_Phase1 _Ring2 Number of reservations in hundreds in phase1 140and ring 21410.1021420.487143014416.05145Airbnb_Phase1 _Ring3 Number of reservations in hundreds in phase1 146and ring 31470.3681481.372149015037.27151Airbnb_Phase2 _Ring1 Number of reservations in hundreds in phase2 152and ring 11530.0611540.381155015621.83157Airbnb_Phase3 _Ring1 Number of reservations in hundreds in phase3 158and ring 11590.0521600.341161016224.05163Dependent variable164P165Natural log of the inflation-adjusted house 166sale price16712.251680.5581696.767 15.21170171172===== PAGE 13 (score 8, numeric-paren 0, table-words 1, var-words 6) =====173174The impact of short‑term rental activity on house prices:…175Page 13 of 33 17661774.1 Model 1178We start with a pooled cross-sectional model using Airbnb density in the clos-179est ring (ring 1: 0 to 300 m) and in the closest phase (phase 1: months S to S-3) 180before house sale ( Airbnb_Phase1_Ring1).181P is the natural log of the inflation-adjusted sale price for all the houses sold 182during the study period. The house characteristic variables contained in X are 183those described in Table 1. T are time fixed effects including sales year dummy 184variables to capture macroeconomic changes within a given year and sales month 185dummy variables to remove seasonality effects. M contains location fixed effects, 186including public school and zip-code-level fixed effects (M), to control for unob-187served time-invariant characteristics that may jointly affect housing prices and 188Airbnb activities, such as commercial activities, infrastructure, and public facili-189ties. Finally, ϵ is the error term.1904.2 Model 2—time effect191In this model, we used Airbnb densities in the closest ring (ring 1) but for differ-192ent time periods before the sale, namely, Airbnb_Phase1_Ring1, Airbnb_Phase2_193Ring1, and Airbnb_Phase3_Ring1, to investigate the impact of Airbnb on house 194prices over time.1954.3 Model 3—distance effect196We further used Airbnb densities in the closest period prior to the sale (Phase 1971: months S to S-3), but for different Rings, namely, Airbnb_Phase1_Ring1, 198Airbnb_Phase1_Ring2, and Airbnb_Phase1_Ring3, to examine if Airbnb impact 199on house prices varies by distance between house sales and Airbnb properties.200(1)201P = 훼+ (Airbnb_Phase1_Ring1)훽1 + 훾X + 훿T + 휃M + 휖202(2)203P = 훼+204(205AirbnbPhase1Ring1206)207훽1 +208(209AirbnbPhase2Ring1210)211훽2212+213(214AirbnbPhase3Ring1215)216훽3 + 훾X + 훿T + 휃M + 휖217(3)218P = 훼+219(220AirbnbPhase1Ring1221)222훽1 +223(224AirbnbPhase1Ring2225)226훽2227+ Airbnb_Phase1_Ring3)훽3 + (+훾X + 훿T + 휃M + 휖228229230===== PAGE 14 (score 29, numeric-paren 6, table-words 7, var-words 8) =====231232 233N. Safari et al.2346 235Page 14 of 332364.4 Unconditional quantile regression237Another goal of this study is to estimate the potential Airbnb effect across the complete 238distribution of house prices. To do so, we used the unconditional quantile regression 239(UQR) method proposed by Firpo et al. (2009). Compared to the (conditional) quantile 240regression method developed by Koenker and Bassett (1978), this methodology allows 241us to estimate the effects on an outcome variable that is not conditioned by the set of 242covariates included in the model (Fortin et al. 2011).243UQR is based on extending the concept of the influence function to what has 244been termed the recentered influence function (RIF). The UQR model is estimated 245by regressing RIF on the covariates.246where Pq휏 is the natural log of the real sale price, P, at the 휏 th quantile (q). 1 is an 247indicator function that takes a value of 1 for P below Pq휏 ; and fy(Pq휏) is the density 248of P at the 휏 th quantile.2495 Results250We begin by presenting estimates from the hedonic model in Eq. (1), with results 251shown in Table 3. Columns (1) to (3) report estimates for three different measures of 252Airbnb activity in the nearest ring (ring 1: 0 to 300 m) and the closest phase (phase 253(4)254RIF255(256Y;Pq휏, FPq휏257)258= Pq휏+259(휏−1{P ≤Pq휏260})261fy(Pq휏)262= (Airbnb_Phase1_Ring1)훽1 + 훾X + 훿T + 휃M + 휖263Table 3 Model 1: OLS regression estimates, all three measures264Standard errors are reported in parentheses and were obtained using 500 bootstrap replicates. ⁎ p < 0.1; 265⁎⁎ p < 0.05; ⁎⁎⁎ p < 0.01266The estimations correspond to Eq. (1), with the natural log of the inflation-adjusted house sale price as 267the dependent variable268The coefficients of the control variables introduced in Table 1, are omitted from this table. A complete 269version of the table is available upon request270Measure (1)271Active272Measure (2)273Positive Occupancy274Measure (3)275Sum of reservations276Airbnb_Phase1 _Ring12770.0034***278(0.0002)2790.0035***280(0.0002)2810.0169***282(0.0018)283Zip code dummies284Yes285Yes286Yes287School district dummies288Yes289Yes290Yes291Year of sale dummies292Yes293Yes294Yes295Month of sale dummies296Yes297Yes298Yes299N300102,142301102,142302102,142303adj. R-sq3040.7893050.7893060.789307308309===== PAGE 15 (score 11, numeric-paren 0, table-words 4, var-words 2) =====310311The impact of short‑term rental activity on house prices:…312Page 15 of 33 31363141: months S to S-3) before the house sale, with standard errors in parentheses. To 315address potential heteroscedasticity and autocorrelation, we primarily use bootstrap 316standard errors for their flexibility and robustness. We also conducted additional 317regressions using clustered standard errors by ZIP code and month of sale, which 318showed only a minimal increase compared to our main bootstrap standard errors. 319Drawing on guidance from Cameron and Miller (2015) and Abadie et al. (2023), 320which recommend adopting clustered standard errors only if they significantly 321exceed robust ones, we evaluated the minor differences and decided to retain our 322main results. For comparison, Table 7 in the appendix includes Table 3 results with 323both clustered and bootstrap standard errors.5324The estimate in column (1) indicates that having one additional active Airbnb 325property within 300 m of a house in phase 1 increases the sale price by 0.34%.6 326Considering that the average number of Airbnb listings within 300 m of a house in 327phase 1 is 0.803, as shown in Table 2, the average magnitude of the impact for the 328first measure is approximately 0.27%. Moving to column (2), we examine a similar 329measure of Airbnb activity—positive occupancy, where an additional Airbnb rental 330increases nearby housing sales by 0.35%. The mean number of Airbnb listings with 331a positive occupancy rate for phase 1 and ring 1 is 0.678, resulting in an average 332impact of approximately 0.24% for the second measure.333In accordance with our findings, our results align with previous studies such as 334Barron et al. (2021), who reported a 1% increase in Airbnb listings resulting in a 3350.026% increase in house prices across the entire USA. Koster et al. (2021) spe-336cifically observed analogous outcomes in Los Angeles, indicating that a 1 percent-337age point increase in the Airbnb listings rate within 100 m led to a 2.51% rise in 338property prices, while a similar increase within 500 m resulted in a 3.72% increase. 339Further reinforcing our observations, Garcia-López et al. (2020) conducted a com-340parable investigation and found average increases of 4.59% in transaction prices and 3413.67% in posted prices attributed to Airbnb activity in Barcelona. These consistent 342trends across different regions and methodologies contribute to the broader under-343standing of the impact of Airbnb activity on housing markets.344Finally, in column (3), we present the impact of the sum of reservations for all 345Airbnb listings over all four months in phase 1 (months S to S-3). Here, an increase 346of 100 more Airbnb reservations within 300 m of a house is associated with a 1.69% 347higher home sales price. The mean for the number of Airbnb reservations for phase 3481 and ring 1 is 0.08 (scaled by a hundred), resulting in an average impact of approxi-349mately 0.13%.350This measure differs from the previous two because it assesses the intensity of 351guest traffic. We included this measure with the expectation that the number of 352reservations would better capture any negative externalities associated with short-353term rental (STR) activity. While the average impact magnitude for this measure is 354comparatively lower than the effects observed for the first two measures, the overall 355positive trend suggests that the net effect of STR activity on house prices remains 356positive, indicating that the income stream received by short-term rental activities 3575 Results for other tables using clustered standard errors are available upon request.3586 Percentage change is calculated using exp(β)-1.359360361===== PAGE 16 (score 42, numeric-paren 15, table-words 9, var-words 7) =====362363 364N. Safari et al.3656 366Page 16 of 33367is more valued by homeowners than potential negative externalities in the neighbor-368hood. This observation aligns with the conclusions drawn by Kim et al. (2017), who 369utilized a differences-in-differences approach to examine the impact of restrictive 370regulations on rental rights for property owners in coastal areas of Florida. Their 371study revealed a negative effect on house prices following the implementation of 372such regulations. Their findings suggested that property owners are likely to prior-373itize their right to rent houses without restrictions over potential negative externali-374ties associated with renters in the neighborhood.375We recognize the inherent challenge of endogeneity in estimating the impact of 376short-term rentals on house prices. To mitigate this concern, our analysis incorpo-377rates zip code × calendar month fixed effects to account for potential time-varying 378amenities that could be correlated with both house prices and Airbnb activity. 379Additionally, Year × Month fixed effects are included to fully capture the time-var-380ying trend in house prices. These results combined with our main result discussed 381above are illustrated in Table 8 in Appendix. In each 3-column set for each meas-382ure, the first column is our main result from Table 3 which does not incorporate 383zip code × month and Year × Month fixed effects. The second column includes 384Year × Month but omits zip code × calendar month fixed effects, and the third col-385umn includes zip code × calendar month but excludes Year × Month fixed effects.386As illustrated in the table, the results from each 3-column set corresponding to 387each measure of Airbnb activity are very close and similar. This pattern holds true 388for the results in our other tables as well. To maintain simplicity and avoid confu-389sion, we present only the results which does not include zip code × month and Year 390Table 4 Model 2: Time Effect, all three measures391Standard errors are reported in parentheses and were obtained using 500 bootstrap replicates. ⁎ p < 0.1; 392⁎⁎ p < 0.05; ⁎⁎⁎ p < 0.01393The estimations correspond to Eq. (2), with the natural log of the inflation-adjusted house sale price as 394the dependent variable395The coefficients of the control variables introduced in Table 1, are omitted from this table. A complete 396version of the table is available upon request397Measure (1)398Active399Measure (2)400Positive Occupancy401Measure (3)402Sum of reservations403Airbnb_Phase1_Ring14040.0033***405(0.0006)4060.0019***407(0.0005)4080.0101***409(0.0030)410Airbnb_Phase2_Ring1411− 0.0006412(0.0008)4130.0011*414(0.0006)4150.0067*416(0.0040)417Airbnb_Phase3_Ring14180.0008419(0.0007)4200.0012**421(0.0005)4220.0055423(0.0038)424Zip code dummies425Yes426Yes427Yes428School district dummies429Yes430Yes431Yes432Year of sale dummies433Yes434Yes435Yes436Month of sale dummies437Yes438Yes439Yes440N441102,142442102,142443102,142444adj. R-sq4450.7894460.7894470.789448449450===== PAGE 17 (score 45, numeric-paren 18, table-words 10, var-words 5) =====451452The impact of short‑term rental activity on house prices:…453Page 17 of 33 4546455× Month fixed effects. The complete results for Tables 4, 5, and 6 also are shown in 456appendix as Tables 9 to 11.457The results in Table 4 explore how Airbnb activity in different periods before 458house sales affects its price. The estimation results for the three measures are 459reported in columns (1) to (3). Consistent with our expectations, the period closer 460to the sale has a stronger effect that diminishes over time. For all three measures, we 461find significant and higher positive coefficients for Airbnb listings in the months of 462S to S-3 (phase 1) compared to Airbnb listings in the months of S-4 to S-7 (phase 2) 463and S-8 to S-11 (phase 3). For instance, the estimation results for our third measure 464in column (3) show that 100 additional Airbnb reservations within 300 m of a house 465are associated with 1.01%, 0.67%, and 0.55% higher home sale prices in phases 1, 2, 466and 3, respectively.467The results in Table 5 show the effect of distance. We expected that closer Airbnb 468properties would have a greater impact on house sale prices. All results in Table 5 469are significant, and the direction of the coefficients is consistent for each measure of 470Airbnb activity. An interesting result here is that all three measures show a negative 471effect of Airbnb listings in ring 3 (Airbnb listings at 500 to 1000 m distance from 472houses) on house prices. Although these negative results are statistically different 473from zero, their magnitudes are very small, indicating that Airbnb listings far from a 474property (i.e., higher than 500 m) do not have a strong impact on house prices.475In particular, Table 5, Column (1) shows that for phase1, the month of the 476house sale and the 3-month period prior to it, one additional Airbnb listing in 477ring 1 has a 0.36% impact on house prices. The average magnitude of this impact 478Table 5 Model 3: Distance Effect, all three measures479Standard errors are reported in parentheses and were obtained using 500 bootstrap replicates. ⁎ p < 0.1; 480⁎⁎ p < 0.05; ⁎⁎⁎ p < 0.01481The estimations correspond to Eq. (3), with the natural log of the inflation-adjusted house sale price as 482the dependent variable483The coefficients of the control variables introduced in Table 1, are omitted from this table. A complete 484version of the table is available upon request485Measure (1)486Active487Measure (2)488Positive Occupancy489Measure (3)490Sum of reservations491Airbnb_Phase1 _Ring14920.0036***493(0.0002)4940.0038***495(0.0003)4960.0183***497(0.0022)498Airbnb_Phase1 _Ring24990.0008***500(0.0003)5010.0008**502(0.0003)5030.0112***504(0.0027)505Airbnb_Phase1 _Ring3506− 0.0007***507(0.0001)508− 0.0008***509(0.0001)510− 0.0069***511(0.0009)512Zip code dummies513Yes514Yes515Yes516School district dummies517Yes518Yes519Yes520Year of sale dummies521Yes522Yes523Yes524Month of sale dummies525Yes526Yes527Yes528N529102,142530102,142531102,142532adj. R-sq5330.7895340.7895350.789536537538===== PAGE 18 (score 78, numeric-paren 52, table-words 7, var-words 6) =====539540 541N. Safari et al.5426 543Page 18 of 33544Table 6 Unconditional Quantile Regression Result, all three measures545The results for each of our three measures are presented in each row, with each measure individually analyzed through a distinct quantile regression. In the table, each col-546umn represents the results corresponding to specific quantiles547The estimations correspond to Eq. (4), with the natural log of the inflation-adjusted house sale price as the dependent variable548Each regression includes Zip code, School district, Year of sale, and Month of sale dummies as fixed effects. The total number of observations for each regression is 549102,142550Standard errors are in parentheses. Standard errors for the quantile regression were obtained using 500 bootstrap replicates. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01551The coefficients for the control variables introduced in Table 1, are not included in this table. The complete version of this table is available upon request5520.10553Quantile5540.20555Quantile5560.30557Quantile5580.40559Quantile5600.50561Quantile5620.60563Quantile5640.70565Quantile5660.80567Quantile5680.90569Quantile570Measure (1): Airbnb _Phase1_Ring15710.0030***572(0.0007)5730.0018***574(0.0005)5750.0017***576(0.0004)5770.0023***578(0.0003)5790.0030***580(0.0003)5810.0041***582(0.0003)5830.0059***584(0.0003)5850.0053***586(0.0004)5870.0040***588(0.0005)589Measure (2): Airbnb _Phase1_Ring15900.0031***591(0.0009)5920.0017***593(0.0005)5940.0015***595(0.0005)5960.0022***597(0.0004)5980.0029***599(0.0003)6000.0041***601(0.0004)6020.0060***603(0.0004)6040.0058***605(0.0004)6060.0045***607(0.0005)608Measure (3): Airbnb _Phase1_Ring16090.0123*610(0.0063)6110.0020612(0.0039)6130.0002614(0.0034)6150.0077***616(0.0028)6170.0139***618(0.0025)6190.0228***620(0.0026)6210.0356***622(0.0030)6230.0361***624(0.0032)6250.0312***626(0.0040)627628629===== PAGE 19 (score 12, numeric-paren 0, table-words 3, var-words 5) =====630631The impact of short‑term rental activity on house prices:…632Page 19 of 33 6336634is approximately 0.29%, while an additional Airbnb listing in ring 2 increases the 635sale price of nearby houses by 0.8%. Finally, one additional Airbnb listing in ring 3 636decreases the neighborhood house sale price by 0.07%. The results of the first two 637measures are similar and consistent. The results in Column (3) reveal that the third 638measure shows a positive effect of 1.83% on house price for each 100 more reser-639vations in ring 1, with an average magnitude of approximately 0.146%. 100 more 640reservations in ring 2 are associated with a 1.12% positive effect, and the average 641magnitude of this effect is 0.114%. Finally, 100 more reservations in ring 3 are asso-642ciated with a 0.69% negative effect on house sales.643Ordinary least-squares regressions estimate the conditional mean impact of the 644STR on housing prices. To estimate the impact of Airbnb on higher versus lower 645house prices, we proceed to the unconditional quantile regression (UQR) model 646shown in Eq. (4). Because the Airbnb impact is concentrated in ring 1 and phase 6471, we apply the UQR model to our baseline Eq. (1). The results are presented in 648Table 6. For the first two measures, the coefficients for all quantiles (first and second 649rows) are significant at the 1% level. According to our third measure (third row), 650most quantiles have significant coefficients. The unconditional quantile regression 651results are consistent for all the three measures. The results reveal that the positive 652impact of Airbnb density on house prices is not consistent across the entire house 653price distribution.654This relationship is notably stronger for the 70th and 80th quantiles, with the 90th 655quantile also showing a relatively strong impact from short-term rentals. The 10th 656quantile exhibits a higher impact relative to its neighboring quantiles. However, the 657literature suggests that elevated tails are often linked to data sparsity, potentially 658introducing noise in the tails of quantile regression estimation (specifically the 10th 659and 90th quantiles), especially for heavy-tailed distributions (Wang, Deyuan, and 660Xuming, 2012).661Conversely, it appears that house prices in the middle range receive an impact 662close to the average, very similar to the OLS regression result. Lower quantiles, 663namely the 20th, 30th, and 40th, show the lowest impact. This result is better illus-664trated in Fig. 5, where each panel plots the explanatory variable’s UQR coefficient 665estimates and their associated 95% confidence intervals.666Considering that the quantile result for the tails could be noisy, the interpretation 667might be biased for the 10th and 90th quantiles. However, there is a clear upward 668trend in the impact of short-term rental activity from the 20th to the 80th quantile. 669For instance, in Table 6, the quantile regression result for the second measure of 670Airbnb activity (second row) shows that both the 70th and 80th quantiles have coef-671ficients of 0.0060 and 0.0058, respectively, noticeably higher than those of the other 672quantiles. Furthermore, houses in the middle price range receive an impact close to 673the average result coming from OLS regression. For example, the coefficients of the 674second measure of Airbnb activity for the 50th and 60th quantiles are 0.0029 and 6750.0041, respectively, compared to 0.0035 of OLS regression for our second measure. 676Additionally, lower-priced houses are less impacted by short-term rental activity. 677For instance, the coefficients of the second measure of Airbnb activity for the 20th, 67830th, and 40th quantiles are 0.0017, 0.0015, and 0.0022, respectively.679680681===== PAGE 25 (score 28, numeric-paren 12, table-words 5, var-words 4) =====682683The impact of short‑term rental activity on house prices:…684Page 25 of 33 6856686Table 7 Model 1: OLS regression estimates, all three measures (Clustered Standard Errors vs, Bootstrap Standard Errors)687Standard errors for regression results in columns (1), (3), and (5) were computed using 500 bootstrap replicates and are presented in parentheses. For columns (2), (4), and 688(6), robust-clustered standard errors clustered by zip code and month are reported in parentheses689⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01690The estimations correspond to Eq. (1), with the natural log of the inflation-adjusted house sale price as the dependent variable691The coefficients of the control variables introduced in Table 1, are not included in this table692Measure (1)-Active693Measure (2)-Positive Occupancy694Measure (3)-Sum of reservations695(1)696(2)697(3)698(4)699(5)700(6)701Airbnb_Phase1 702_Ring17030.0034***704(0.0002)7050.0034***706(0.0003)7070.0035***708(0.0002)7090.0035***710(0.0003)7110.0169***712(0.0018)7130.0169***714(0.0022)715Zip code dummies716Yes717Yes718Yes719Yes720Yes721Yes722School district 723dummies724Yes725Yes726Yes727Yes728Yes729Yes730Year of sale dum-731mies732Yes733Yes734Yes735Yes736Yes737Yes738Month of sale 739dummies740Yes741Yes742Yes743Yes744Yes745Yes746N747102,142748102,142749102,142750102,142751102,142752102,142753adj. R-sq7540.7897550.7897560.7897570.7897580.7897590.789760761762===== PAGE 26 (score 38, numeric-paren 18, table-words 7, var-words 4) =====763764 765N. Safari et al.7666 767Page 26 of 33768Table 8 Model 1: OLS regression estimates, all three measures (Including Year × Month and Zip Code × Calendar Month Interactions)769The table displays results for each measure under three different scenarios: without including Year * Month or Zip code * calendar Month (Columns 1, 4, and 7), including 770Year * Month (Columns 2, 5, and 8), and including Zip code * calendar Month (Columns 3, 6, and 9)771Standard errors are in parentheses. Standard errors for the regression were obtained using 500 bootstrap replicates. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01772The estimations correspond to Eq. (1), with the natural log of the inflation-adjusted house sale price as the dependent variable773The coefficients of the control variables introduced in Table 1, are not included in this table. The complete version of this table is available upon request774Measure (1)—Active775Measure (2)—Positive Occupancy776Measure (3)—Sum of reservations777(1)778(2)779(3)780(4)781(5)782(6)783(7)784(8)785(9)786Airbnb_Phase1 _Ring17870.0034***788(0.0002)7890.0034***790(0.0002)7910.0036***792(0.0002)7930.0035***794(0.0002)7950.0035***796(0.0002)7970.0038***798(0.0003)7990.0169***800(0.0018)8010.0170***802(0.0018)8030.0206***804(0.0019)805Zip code dummies806Yes807Yes808Yes809Yes810Yes811Yes812Yes813Yes814Yes815School district dummies816Yes817Yes818Yes819Yes820Yes821Yes822Yes823Yes824Yes825Year of sale dummies826Yes827Yes828Yes829Yes830Yes831Yes832Yes833Yes834Yes835Month of sale dummies836Yes837Yes838Yes839Yes840Yes841Yes842Yes843Yes844Yes845Year * Month846No847Yes848No849No850Yes851No852No853Yes854No855Zip code * calendar Month856No857No858Yes859No860No861Yes862No863No864Yes865N866102,142867102,142868102,142869102,142870102,142871102,142872102,142873102,142874102,142875adj. R-sq8760.7898770.7898780.7948790.7898800.7898810.7948820.7898830.7898840.794885886887===== PAGE 27 (score 70, numeric-paren 46, table-words 7, var-words 4) =====888889The impact of short‑term rental activity on house prices:…890Page 27 of 33 8916892Table 9 Model 2: Time Effect, all three measures (Including Year × Month and Zip Code × Calendar Month Interactions)893The table displays results for each measure under three different scenarios: without including Year * Month or Zip code * calendar Month (Columns 1, 4, and 7), including 894Year * Month (Columns 2, 5, and 8), and including Zip code * calendar Month (Columns 3, 6, and 9)895Standard errors are in parentheses. Standard errors for the regression were obtained using 500 bootstrap replicates. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01896The estimations correspond to Eq. (2), with the natural log of the inflation-adjusted house sale price as the dependent variable897The coefficients of the control variables introduced in Table 1, are not included in this table. The complete version of this table is available upon request898Measure (1)—Active899Measure (2)—Positive Occupancy900Measure (3)—Sum of reservations901(1)902(2)903(3)904(4)905(5)906(6)907(7)908(8)909(9)910Airbnb_Phase1 _Ring19110.0033***912(0.0006)9130.0033***914(0.0006)9150.0033***916(0.0006)9170.0019***918(0.0005)9190.0019***920(0.0005)9210.0018***922(0.0005)9230.0101***924(0.0030)9250.0103***926(0.0030)9270.0108***928(0.0033)929Airbnb_Phase2_Ring1930− 0.0006931(0.0008)932− 0.0007933(0.0008)934− 0.0007935(0.0008)9360.0011*937(0.0006)9380.0010*939(0.0006)9400.0013**941(0.0006)9420.0067*943(0.0040)9440.0061945(0.0040)9460.0079*947(0.0045)948Airbnb_Phase3_Ring19490.0008950(0.0007)9510.0009952(0.0007)9530.0012*954(0.0007)9550.0012**956(0.0005)9570.0012**958(0.0005)9590.0016***960(0.0006)9610.0055962(0.0038)9630.0059964(0.0038)9650.0094**966(0.0043)967Zip code dummies968Yes969Yes970Yes971Yes972Yes973Yes974Yes975Yes976Yes977School district dummies978Yes979Yes980Yes981Yes982Yes983Yes984Yes985Yes986Yes987Year of sale dummies988Yes989Yes990Yes991Yes992Yes993Yes994Yes995Yes996Yes997Month of sale dummies998Yes999Yes1000Yes1001Yes1002Yes1003Yes1004Yes1005Yes1006Yes1007Year * Month1008No1009Yes1010No1011No1012Yes1013No1014No1015Yes1016No1017Zip code * calendar Month1018No1019No1020Yes1021No1022No1023Yes1024No1025No1026Yes1027N1028102,1421029102,1421030102,1421031102,1421032102,1421033102,1421034102,1421035102,1421036102,1421037adj. R-sq10380.78910390.78910400.79410410.78910420.78910430.79410440.78910450.78910460.794104710481049===== PAGE 28 (score 78, numeric-paren 54, table-words 7, var-words 4) =====10501051 1052N. Safari et al.10536 1054Page 28 of 331055Table 10 Model 3: Distance Effect, all three measures (Including Year × Month and Zip Code × Calendar Month Interactions)1056The table displays results for each measure under three different scenarios: without including Year * Month or Zip code * Month (Columns 1, 4, and 7), including Year * 1057Month (Columns 2, 5, and 8), and including Zip code * Month (Columns 3, 6, and 9)1058Standard errors are in parentheses. Standard errors for the regression were obtained using 500 bootstrap replicates. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.011059The estimations correspond to Eq. (3), with the natural log of the inflation-adjusted house sale price as the dependent variable1060The coefficients of the control variables introduced in Table 1, are not included in this table. The complete version of this table is available upon request1061Measure (1)—Active1062Measure (2)—Positive Occupancy1063Measure (3)—Sum of reservations1064(1)1065(2)1066(3)1067(4)1068(5)1069(6)1070(7)1071(8)1072(9)1073Airbnb_Phase1 _Ring110740.0036***1075(0.0002)10760.0036***1077(0.0002)10780.0036***1079(0.0002)10800.0038***1081(0.0003)10820.0038***1083(0.0003)10840.0038***1085(0.0003)10860.0183***1087(0.0022)10880.0184***1089(0.0022)10900.0193***1091(0.0022)1092Airbnb_Phase1_Ring210930.0008***1094(0.0003)10950.0008***1096(0.0003)10970.0008***1098(0.0003)10990.0008**1100(0.0003)11010.0008**1102(0.0003)11030.0007**1104(0.0003)11050.0112***1106(0.0027)11070.0112***1108(0.0027)11090.0115***1110(0.0027)1111Airbnb_Phase1_Ring31112− 0.0007***1113(0.0001)1114− 0.0007***1115(0.0001)1116− 0.0005***1117(0.0001)1118− 0.0008***1119(0.0001)1120− 0.0007***1121(0.0001)1122− 0.0005***1123(0.0001)1124− 0.0069***1125(0.0009)1126− 0.0068***1127(0.0009)1128− 0.0051***1129(0.0010)1130Zip code dummies1131Yes1132Yes1133Yes1134Yes1135Yes1136Yes1137Yes1138Yes1139Yes1140School district dummies1141Yes1142Yes1143Yes1144Yes1145Yes1146Yes1147Yes1148Yes1149Yes1150Year of sale dummies1151Yes1152Yes1153Yes1154Yes1155Yes1156Yes1157Yes1158Yes1159Yes1160Month of sale dummies1161Yes1162Yes1163Yes1164Yes1165Yes1166Yes1167Yes1168Yes1169Yes1170Year * Month1171No1172Yes1173No1174No1175Yes1176No1177No1178Yes1179No1180Zip code * calendar Month1181No1182No1183Yes1184No1185No1186Yes1187No1188No1189Yes1190N1191102,1421192102,1421193102,1421194102,1421195102,1421196102,1421197102,1421198102,1421199102,1421200adj. R-sq12010.78912020.78912030.79412040.78912050.78912060. 0.79412070.78912080.78912090.794121012111212===== PAGE 29 (score 192, numeric-paren 157, table-words 6, var-words 6) =====12131214The impact of short‑term rental activity on house prices:…1215Page 29 of 33 121661217Table 11 Unconditional Quantile Regression Result, all three measures (Including Year × Month and Zip Code × Calendar Month Interactions)1218The results for each of our three measures are presented in three rows under three different scenarios: without including Year * Month or Zip code * Month (Rows 1, 4, 1219and 7), including Year * Month (Rows 2, 5, and 8), and including Zip code * Month (Rows 3, 6, and 9). Each measure is analyzed separately using quantile regression. 1220Columns represent the results corresponding to specific quantiles1221Each regression includes Zip code, School district, Year of sale, and Month of sale dummies as fixed effects. The total number of observations for each regression is 1222102,1421223Standard errors are in parentheses. Standard errors for the quantile regression were obtained using 500 bootstrap replicates. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.011224The estimations correspond to Eq. (4), with the natural log of the inflation-adjusted house sale price as the dependent variable1225The coefficients for the control variables introduced in Table 1, are not included in this table. The complete version of this table is available upon request1226Additional1227Fixed Effect12280.101229Quantile12300.201231Quantile12320.301233Quantile12340.401235Quantile12360.501237Quantile12380.601239Quantile12400.701241Quantile12420.801243Quantile12440.901245Quantile1246Measure (1)1247(1)1248–12490.0030***1250(0.0007)12510.0018***1252(0.0005)12530.0017***1254(0.0004)12550.0023***1256(0.0003)12570.0030***1258(0.0003)12590.0041***1260(0.0003)12610.0059***1262(0.0003)12630.0053***1264(0.0004)12650.0040***1266(0.0005)1267(2)1268Year * Month12690.0030***1270(0.0007)12710.0018***1272(0.0005)12730.0017***1274(0.0004)12750.0022***1276(0.0003)12770.0029***1278(0.0003)12790.0041***1280(0.0003)12810.0059***1282(0.0003)12830.0052***1284(0.0004)12850.0040***1286(0.0005)1287(3)1288Zip code * Calendar month12890.0032***1290(0.0008)12910.0022***1292(0.0005)12930.0020***1294(0.0004)12950.0025***1296(0.0003)12970.0032***1298(0.0003)12990.0046***1300(0.0003)13010.0063***1302(0.0003)13030.0056***1304(0.0004)13050.0041***1306(0.0005)1307Measure (2)1308(4)1309–13100.0031***1311(0.0009)13120.0017***1313(0.0005)13140.0015***1315(0.0005)13160.0022***1317(0.0004)13180.0029***1319(0.0003)13200.0041***1321(0.0004)13220.0060***1323(0.0004)13240.0058***1325(0.0004)13260.0045***1327(0.0006)1328(5)1329Year * Month13300.0031***1331(0.0009)13320.0017***1333(0.0005)13340.0015***1335(0.0005)13360.0021***1337(0.0004)13380.0029***1339(0.0003)13400.0041***1341(0.0004)13420.0060***1343(0.0004)13440.0058***1345(0.0004)13460.0045***1347(0.0005)1348(6)1349Zip code * Calendar month13500.0034***1351(0.0009)13520.0023**1353(0.0006)13540.0019***1355(0.0005)13560.0025***1357(0.0004)13580.0034***1359(0.0004)13600.0048***1361(0.0004)13620.0065***1363(0.0004)13640.0062***1365(0.0005)13660.0045***1367(0.0006)1368Measure (3)1369(7)1370–13710.0123*1372(0.0063)13730.00201374(0.0039)13750.00021376(0.0034)13770.0077***1378(0.0028)13790.0139***1380(0.0025)13810.0228***1382(0.0026)13830.0356***1384(0.0030)13850.0361***1386(0.0032)13870.0312***1388(0.0040)1389(8)1390Year * Month13910.0126**1392(0.0063)13930.00241394(0.0039)13950.00021396(0.0034)13970.0075***1398(0.0028)13990.0138***1400(0.0025)14010.0227***1402(0.0026)14030.0357***1404(0.0030)14050.0361***1406(0.0032)14070.0312***1408(0.0040)1409(9)1410Zip code * Calendar month14110.0165**1412(0.0067)14130.0076*1414(0.0041)14150.00421416(0.0036)14170.0110***1418(0.0030)14190.0187***1420(0.0026)14210.0290***1422(0.0028)14230.0415***1424(0.0032)14250.0403***1426(0.0034)14270.0313***1428(0.0043)142914301431===== PAGE 30 (score 135, numeric-paren 102, table-words 8, var-words 6) =====14321433 1434N. Safari et al.14356 1436Page 30 of 331437Table 12 Unconditional Quantile Regression Result, all three measures (Census Block Dummies vs. Zip Code Dummies)1438The table displays results for each measure under two different scenarios for comparison: our main result from Table 5 (Rows 1, 3, and 5) and new results with Census 1439block dummies replacing Zip code dummies (Rows 2, 4, and 6). Each measure is analyzed separately using quantile regression. Columns represent the results correspond-1440ing to specific quantiles1441Each regression includes Zip code, School district, Year of sale, and Month of sale dummies as fixed effects. The total number of observations for each regression is 1442102,1421443Standard errors are in parentheses. Standard errors for the quantile regression were obtained using 500 bootstrap replicates. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.011444The estimations correspond to Eq. (4), with the natural log of the inflation-adjusted house sale price as the dependent variable1445The coefficients for the control variables introduced in Table 1, are not included in this table. The complete version of this table is available upon request1446Additional1447Fixed Effect14480.101449Quantile14500.201451Quantile14520.301453Quantile14540.401455Quantile14560.501457Quantile14580.601459Quantile14600.701461Quantile14620.801463Quantile14640.901465Quantile1466Measure (1): Airbnb _Phase1_1467Ring11468(1)1469–14700.0030***1471(0.0007)14720.0018***1473(0.0005)14740.0017***1475(0.0004)14760.0023***1477(0.0003)14780.0030***1479(0.0003)14800.0041***1481(0.0003)14820.0059***1483(0.0003)14840.0053***1485(0.0004)14860.0040***1487(0.0005)1488(2)1489Census block1490dummies14910.0027***1492(0.0008)14930.0014***1494(0.0005)14950.0015***1496(0.0004)14970.0019***1498(0.0003)14990.0025***1500(0.0003)15010.0036***1502(0.0003)15030.0054***1504(0.0004)15050.0048***1506(0.0004)15070.0037***1508(0.0005)1509Measure (2): Airbnb _Phase1_1510Ring11511(3)1512–15130.0031***1514(0.0009)15150.0017***1516(0.0005)15170.0015***1518(0.0005)15190.0022***1520(0.0004)15210.0029***1522(0.0003)15230.0041***1524(0.0004)15250.0060***1526(0.0004)15270.0058***1528(0.0004)15290.0045***1530(0.0006)1531(4)1532Census block1533dummies15340.0027***1535(0.0009)15360.0013**1537(0.0006)15380.0013***1539(0.0005)15400.0016***1541(0.0004)15420.0023***1543(0.0003)15440.0034***1545(0.0004)15460.0053***1547(0.0004)15480.0052***1549(0.0005)15500.0042***1551(0.0006)1552Measure (3): Airbnb _Phase1_1553Ring11554(5)1555–15560.0123*1557(0.0063)15580.00201559(0.0039)15600.00021561(0.0034)15620.0077***1563(0.0028)15640.0139***1565(0.0025)15660.0228***1567(0.0026)15680.0356***1569(0.0030)15700.0361***1571(0.0032)15720.0312***1573(0.0040)1574(6)1575Census block1576dummies15770.00911578(0.0065)15790.00011580(0.0040)1581− 0.00071582(0.0035)15830.00401584(0.0028)15850.0086***1586(0.0025)15870.0161***1588(0.0026)15890.0280***1590(0.0030)15910.0300***1592(0.0033)15930.0299***1594(0.0041)159515961597===== PAGE 31 (score 123, numeric-paren 91, table-words 7, var-words 7) =====15981599The impact of short‑term rental activity on house prices:…1600Page 31 of 33 160161602Table 13 Unconditional Quantile Regression Result, all three measures (Private Room vs. Entire Place)1603The table displays results for each measure in two rows under two different scenarios: including only “Entire place” listings (rows 1, 3, and 5) and including only “Private 1604room” listings (Rows 2, 4, and 6). Each measure is analyzed separately using quantile regression. Columns represent the results corresponding to specific quantiles1605Each regression includes Zip code, School district, Year of sale, and Month of sale dummies as fixed effects. The total number of observations for each regression is 1606102,1421607Standard errors are in parentheses. Standard errors for the quantile regression were obtained using 500 bootstrap replicates. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.011608The estimations correspond to Eq. (4), with the natural log of the inflation-adjusted house sale price as the dependent variable1609The coefficients for the control variables introduced in Table 1, are not included in this table. The complete version of this table is available upon request1610Style:16110.101612Quantile16130.201614Quantile16150.301616Quantile16170.401618Quantile16190.501620Quantile16210.601622Quantile16230.701624Quantile16250.801626Quantile16270.901628Quantile1629Measure (1):1630(1)1631Entire place16320.0028***1633(0.0008)16340.0020***1635(0.0005)16360.0021***1637(0.0004)16380.0028***1639(0.0004)16400.0035***1641(0.0003)16420.0049***1643(0.0003)16440.0067***1645(0.0004)16460.0060***1647(0.0004)16480.0051***1649(0.0005)1650(2)1651Private room16520.0152***1653(0.0034)16540.0044**1655(0.0021)1656− 0.00081657(0.0019)1658− 0.00201659(0.0015)16600.00041661(0.0014)16620.00101663(0.0014)16640.0061***1665(0.0016)16660.0036**1667(0.0018)1668− 0.0069***1669(0.0022)1670Measure (2):1671(3)1672Entire place16730.0030***1674(0.0009)16750.0019***1676(0.0006)16770.0020***1678(0.0005)16790.0029***1680(0.0004)16810.0036***1682(0.0004)16830.0050***1684(0.0004)16850.0069***1686(0.0004)16870.0067***1688(0.0005)16890.0058***1690(0.0006)1691(4)1692Private room16930.0130***1694(0.0038)16950.00381696(0.0024)1697− 0.00261698(0.0021)1699− 0.0034**1700(0.0017)1701− 0.00071702(0.0015)17030.00011704(0.0016)17050.0053***1706(0.0018)17070.0040**1708(0.0020)1709− 0.0062**1710(0.0024)1711Measure (3):1712(5)1713Entire place17140.0128*1715(0.0068)17160.00341717(0.0042)17180.00461719(0.0037)17200.0120***1721(0.0030)17220.0177***1723(0.0027)17240.0276***1725(0.0028)17260.0415***1727(0.0032)17280.0406***1729(0.0035)17300.0374***1731(0.0044)1732(6)1733Private room17340.02981735(0.0265)1736− 0.01001737(0.0163)1738− 0.05391739(0.0143)1740− 0.0342***1741(0.0117)1742− 0.01231743(0.0104)1744− 0.00181745(0.0108)17460.01611747(0.0125)17480.0326**1749(0.0135)1750− 0.01061751(0.0169)175217531754===== PAGE 32 (score 9, numeric-paren 0, table-words 0, var-words 9) =====17551756 1757N. Safari et al.17586 1759Page 32 of 331760Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, 1761which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long 1762as you give appropriate credit to the original author(s) and the source, provide a link to the Creative 1763Commons licence, and indicate if changes were made. The images or other third party material in this 1764article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line 1765to the material. If material is not included in the article’s Creative Commons licence and your intended 1766use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permis-1767sion directly from the copyright holder. 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