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Abstract

This paper examines the impact of short-term rental platforms on long-term rental prices in Sydney, focusing on the time before and after the announcement and implementation of Short-Term Rental Accommodation (STRA) laws. Using a difference-in-differences (DiD) regression model, we find significant effects on long-term rental prices, explained by the before and after effects of the introduction of the STRA laws, controlling for neighborhood and housing market variables. Results show that the introduction of STRA laws is associated with a 2.73–3.55% increase in long-term rental prices. Additionally, each additional Airbnb per square kilometer increase was found to significantly raise rental prices by 0.02%. Other significant factors include the positive impact of the total number of rental bonds, which with a 1% increased has raised rents by 0.185–0.21%, and the local crime rate, which reduces rents by 0.033–0.042%. Our findings suggest that the STRA laws have negatively impacted the rental market, causing rents to rise. Although these regulations were intended to manage the effects of short-term rentals, they have in effect led to higher long-term rental prices, worsening housing affordability in Sydney. This effect can be attributed to the STRA laws restricting the number of days for which a short-term rental can be hired, instead of the number of dwellings, which leads to more dwellings moving out of the long-term rental market into the short-term market. Our findings emphasize the need for more balanced policies that ensure the benefits of short-term rental platforms do not come at the expense of affordable housing for residents in the long-term rental market.

Keywords

STRA laws Airbnb long-term rents Sydney difference-in-differences

Introduction

Real-estate markets in many major cities around the world are facing a significant housing crisis which translates to severe unaffordability and housing stress in the housing market which primarily affects lower and moderate income earners significantly (Van Doorn et al., 2019). A contributing factor that exacerbates this crisis is the transformation of many properties into short-stay accommodations, prioritizing tourist rentals over long-term housing options for the growing local population, driven by profitability considerations. Several studies have investigated the impact of Airbnb on housing markets, focusing primarily on house prices and rents. Barron et al. (2021) found that the growth of Airbnb in the US led to an increase in house prices and rents, suggesting that short-term rentals reduce the supply of long-term rental housing. Similarly, Franco and Santos (2021) observed that Airbnb’s presence in Portugal led to increased residential property values and rents, further supporting the idea that short-term rentals can drive up housing costs. Airbnb’s impact extends beyond just financial aspects, also contributing to various negative externalities. Gutiérrez et al. (2017) highlighted issues such as noise caused by tourists in residential areas, which can lead to conflicts between residents and short-term renters. Zervas et al. (2017) discussed the competitive pressure that Airbnb places on traditional hotel industries, potentially leading to economic shifts in tourist-centric cities. Additionally, Edelman et al. (2017) explored racial discrimination within the sharing economy, indicating that Airbnb hosts might exhibit biased behaviors toward certain racial groups, which raises concerns about equality and fairness in the platform’s operations.

Alizadeh et al. (2018) investigate the socio-economic patterns of Airbnb listings in Sydney and Melbourne, revealing that Airbnb hosting is predominantly an affluent activity influenced by education. They find significant impacts in tourist areas, suggesting the need for regulatory intervention. Similarly, Gurran and Phibbs (2017) investigate Airbnb’s impact on Sydney, noting issues like noise, congestion, and reduced rental housing supply, alongside income opportunities for hosts. They analyze stakeholder submissions, local regulations, listings data, and housing market stats, emphasizing monitoring challenges and rapid industry changes. Thackway et al. (2022) conducted a spatially explicit analysis in Sydney using geographically weighted regression (GWR) to assess Airbnb’s impact on housing prices. They found significant variability in the “Airbnb effect” across different neighborhoods, with a 1% increase in Airbnb density leading to around a 2% increase in property sales prices. Their study highlights the importance of considering spatial dynamics when evaluating the impact of short-term rentals on housing markets.

In response to these impacts, various cities and countries have implemented regulations to manage short-term rentals. Nieuwland and Van Melik (2020) reviewed how different cities have dealt with the perceived negative externalities of short-term rentals, highlighting a range of regulatory approaches from outright bans to more moderate measures like capping the number of rental days per year. Von Briel and Dolnicar (2021) conducted an international longitudinal investigation into the evolution of Airbnb regulation from 2008 to 2020, providing insights into the effectiveness and challenges of different regulatory frameworks. Koster et al. (2021) used a quasi-experimental approach with a regression discontinuity design (RDD) and difference-in-difference (DiD) methods to study the impact of Airbnb regulations in Los Angeles County. They found that Airbnb laws reduced listings by 50%, housing prices by 2%, and rents by 2%, indicating that well-designed regulations can mitigate some of the adverse effects of short-term rentals on the housing market. Similarly, Robertson et al. (2024) employed DiD and triple difference models to analyze the effects of Airbnb regulations in Bordeaux, finding a 44% reduction in short-term rental bookings. Falk and Scaglione (2024) discovered that policies in Geneva led to a significant 15% reduction in revenue for fully booked Airbnbs. Conversely, Van Holm (2020) studied similar measures in New Orleans and observed a temporary reduction of 2,070 Airbnb listings, with subsequent growth resuming, particularly in residential areas. In Barcelona, Benítez-Aurioles (2021) found that the regulations had limited effects, resulting in only a 4% reduction in revenue for affected Airbnbs post-policy. Furthermore, Boto-García et al. (2023) combined a DiD model with an event study approach to investigate the effects of Airbnb registration number disclosures in Paris, finding a 10% increase in ratings for registered hosts compared to illegal operators.

Although studies on Airbnb regulations have emerged in recent years and been conducted in cities worldwide, literature evaluating such policies within the Australian context remains limited. This study aims to fill this gap by providing a detailed analysis of the STRA laws’ effects on long-term rental prices in Sydney using DiD and event study models. By examining the distinct characteristics and conditions of the Australian market, our research provides valuable insights into the effectiveness and broader impacts of short-term rental regulations in this region.

Background: Case study—Sydney

Since the inception of Airbnb in Australia in 2012, the platform has significantly impacted tourism in the country by accommodating over 2.1 million guests staying in 800,000 Airbnb listings by its third year of operation (Deloitte Access Economics, 2017). In New South Wales (NSW) alone, Airbnb guests spent $3.65 billion, supporting 27,900 jobs and contributing $4.36 billion to the gross state product (GSP) (Oxford Economics, 2023). While Airbnb has boosted the tourism sector, local businesses, and property investors, it has also exacerbated housing challenges in major cities like Sydney by reducing the availability of long-term rental properties and driving up rental prices.

Sydney is Australia’s largest city and the capital of the state of NSW, with a population of over 5.2 million (Australian Bureau of Statistics, 2021d). The city encompasses 658 suburbs spread across 33 local government areas (LGAs) and is projected to grow to approximately 6.1 million by 2041 (NSW Department of Planning, Industry and Environment, 2022). However, facing a projected annual deficit of 40,000 dwellings between 2023 and 2029 and a vacancy rate of only 1.8% by the end of 2022 (National Housing Supply and Affordability Council (NHSAC), 2024; NSW Department of Planning, Industry and Environment, 2023), Sydney is experiencing a significant housing crisis caused by issues such as worsening housing affordability and increasing financing difficulties. Low wage growth and high levels of household debt have made it harder for residents to afford home purchases or rentals (Gurran and Phibbs, 2013; Rowley et al., 2023) with rents raising by 41% and sales prices increasing by a staggering 114% for houses and 61% for units between 2010 and 2022 (Abelson and Joyeux, 2023). This has contributed to a negative interstate net migration with 31,678 more residents leaving NSW than arriving in 2023 alone, reflecting a 27.86% gap between arrivals and departures (Australian Bureau of Statistics, 2023a).

In response to this crisis, the NSW government has implemented several measures aimed at alleviating the housing shortage. These initiatives include the construction of new affordable housing units, incentives for property developers to increase housing supply, and reforms to zoning regulations intended to streamline the approval process for new developments (NSW Department of Planning, Industry and Environment, 2021; NSW Government, 2020). Despite these efforts, the gap between housing supply and demand continues to widen.

Consequently, alternative measures such as the Short-term Rental Accommodation (STRA) laws have been introduced. These laws aim to limit the profitability of short-term rentals primarily used for tourists and visitors by restricting the number of days for which a short-term rental may be hired for stay. This restriction is intended to transform these dwellings into long-term rentals for local residents, addressing the urgent need for more permanent housing solutions.

The government of NSW had announced its initial STRA laws on the 5^th of June, 2018 (NSW Department of Planning, Industry and Environment, 2024). This included a state-wide planning framework, changes to strata legislation, and a mandatory Code of Conduct. These laws define short-term rentals as accommodation arrangements of 3 months or less.¹ Additionally, the laws created a distinction between hosted STRA, where the host resides on the premises during the accommodation provision, and non-hosted STRA, where the host does not. Later amendments included the opening of a STRA registry requiring all hosts to register their properties² with the NSW Department of Planning, Industry and Environment. On the 9^th of April, 2021, the government of NSW announced the restriction of all non-hosted STRAs to a maximum of 180 days of occupancy in Greater Sydney,³ which was implemented on the 1^st of November, 2021 for the purpose of increasing housing supply and affordability in the region (NSW Department of Planning, Industry and Environment, 2024). The underlying hypothesis behind these policy changes was that as a result of these restrictions, owners would move dwelling stock back into the long-term rental market for more stable returns.

Paper contribution

In this paper, we will evaluate the effectiveness of the STRA laws’ 180-day occupancy limit on reducing long-term rental prices in Sydney using a DiD approach. The DiD method allows us to estimate the causal effect of the policy by comparing the changes in rental prices in areas affected by the law (treatment group—Sydney) to those that were not (control group—Melbourne), before and after the policy was introduced. A key assumption in this approach is the parallel trends assumption, which holds that in the absence of the policy, rental prices in both the treatment and control groups would have followed the same trajectory over time. To verify this, we include an event study approach.

Following the literature review outlined in the Introduction and Background sections, we next describe the data used for our study and detail our empirical approach, which integrates a DiD model, an event study framework, and a heterogeneous treatment effects (HTEs) analysis to assess how the impact of the STRA laws evolves over time and differs across economic segments. Finally, we will showcase our results, which show that the STRA laws have actually increased rental prices by 3.55% and 2.73% after the announcement and implementation, respectively. We will conclude by summarizing our findings and discussing their policy relevance, with reflections for the future.

This research also has a larger, more general aim of employing the DiD approach to formalize the spatial outcomes of urban planning and management policy interventions. In economics, the study of whether policy interventions have had the desired effect, or unexpected undesired outcomes, has a deep and rich scholarly history. However, such analysis is not very common in the urban, spatial, and geographic domain—while DiD methods are widely used in economics to assess policy impacts, they are rarely applied to spatial contexts. Economic models typically do not incorporate spatial relationships explicitly, whereas urban science often relies on qualitative or descriptive spatial analysis rather than econometric techniques. In this work, our aim is to bring together insights from both domains to examine the spatial outcomes arising out of policy interventions, specifically how they reshape rental markets across different locations. In the case of the STRA laws, the spatial outcomes will specifically refer to variations in rental price changes across neighborhoods, the potential redistribution of short-term rental supply, and shifts in housing market dynamics due to regulatory constraints affecting certain areas more than others.

Data and descriptives

This section provides an overview of the data sources and key variables used in the regression analysis. Table 1 summarizes the primary datasets, detailing their sources and types, while the proceeding sections define each variable and explain their relevance to the study. Each of these sections includes subsections in which we discuss the transformed variables, outlining the modifications applied to the raw data. Finally, Table 2 presents the descriptive statistics (mean, standard deviation, minimum, and maximum) for the transformed variables.

Table 1.

Data sources and descriptions.

Data information	Data	Type	Source
Airbnb (property-level data)	Latitude	Coordinates	https://www.InsideAirbnb.com
	Longitude	Coordinates
	Number of bedrooms	Numerical
	Property type	Categorical
	Price per night	Numerical
	First listing review	Date
	Last listing review	Date
NSW and Victoria Rental Data (LGA-level data)	Median weekly rent	Numerical	Department of Communities and Justice NSW (n.d), Department of Families fairness and housing Victoria (n.d)
NSW and Victoria Rental Data (LGA-level data)	Number of rental bonds	Numerical
Schooling Data (school-level data)	Latitude	Coordinates	Australian Curriculum, Assessment and Reporting Authority (2013)
	Longitude	Coordinates
	Index of Community Socio-Educational Advantage (ICSEA) value	Numerical
Crime Data (LGA-level data)	Annual assault rate	Numerical	NSW Bureau of Crime Statistics and Research (2024), Crime Statistics Agency Victoria (2024)
	Annual theft rate	Numerical
	Annual property damage rate	Numerical
Economic Resources (SA2-level data)	Index of Economic Resources (IER)	Numerical	Australian Bureau of Statistics (2021e)

Table 2.

Descriptive statistics (mean, std, min, and max) for variables.

Variable	Mean	Std	Min	Max
Weighted Avg Rent Price	482.87	98.48	280.00	925.00
Sydney	0.49	0.50	0.00	1.00
Post-Policy	0.53	0.50	0.00	1.00
Weighted Total Bonds	20390.29	11984.75	646.00	71122.00
Weighted Avg Crime Rate	1042.06	481.16	345.03	3213.80
Avg Quality of Schooling	1055.87	64.74	756.50	1215.00
Airbnb Density	1.92	6.4	0.0014	133.55
Avg Airbnb Bedrooms	1.68	0.97	1.00	22.00
Avg Airbnb Price	243.12	639.17	17.00	21226.20
% Shared Airbnb	0.37	0.41	0.00	1.00
IER	5.57	3.06	1.00	10.00

Note: Each value represents the mean, standard deviation (Std), minimum (Min), and maximum (Max) calculated from data aggregated on a quarterly basis for the respective variables.

Airbnb and the STRA Policy

We analyzed Airbnb data downloaded from https://www.InsideAirbnb.com covering the period from 2019Q1 to 2023Q3 for Sydney and Melbourne. The data included various listing characteristics such as latitude, longitude, number of bedrooms, property type, price per night, and the dates of the first and last review for each listing. We categorized all listings based on the year and quarter of their operation, and aggregated by their spatial locations. An Airbnb was considered operational in a given quarter if the date of the last review fell within that quarter (Zervas et al., 2017). Additionally, we defined the quarter of establishment for an Airbnb as the quarter when it received its first review. Subsequently, the geographic locations of all Airbnbs were used to allocate them to a Statistical Area Level 2 (SA2).⁴ Their latitude and longitude coordinates and the boundaries of all SA2s in the Greater Sydney and Greater Melbourne areas were used, as collected from Australian Bureau of Statistics (2021c). This allocation was performed using QGIS. Figures 1 and 2 display the number of Airbnb listings per square kilometer in each SA2 in Greater Sydney and Greater Melbourne for the period from 2019Q1 to 2023Q3. The figures show a concentration of Airbnb listings primarily in and around the central business districts of Sydney and Melbourne, as well as in tourist destinations such as Bondi and Manly in Sydney and St. Kilda in Melbourne.

Figure 1.

Distribution of Airbnb listings by density across SA2 areas in Sydney (2019Q1–2023Q3).

Figure 2.

Distribution of Airbnb listings by density across SA2 areas in Melbourne (2019Q1–2023Q3).

Following this, numerical values such as price per night and number of bedrooms were averaged over the aggregated sets per SA2, and property types were categorized into shared or private accommodations, which were then represented as percentages of the total listings within each SA2. Airbnb listings located outside the Greater Sydney or Melbourne regions, those with their last review before 2019Q1, or those with missing data on dates, latitude, longitude, number of bedrooms, property type, or price per night were removed from the dataset. Figure 3 shows the number of new Airbnbs established in Sydney and Melbourne each quarter from 2019Q1 to 2023Q3. The data indicates a significant rise in new listings for both cities, from only 219 and 276 new Airbnbs opening in Sydney and Melbourne, respectively, one quarter before the policy announcement (2021Q1) to a more pronounced upward trend in the later stages of the COVID-19 pandemic, as both national and international travel began to recover and the end of 2023Q3, the number of newly established Airbnbs had surged to 1594 in Melbourne and 1188 in Sydney. Figure 4 presents the quarterly Airbnb prices over the same period. The data shows that Sydney experienced a general price increase, rising from 201$ per night in 2019Q1 to 249$ in 2023Q3, though the growth slowed after the policy announcement, when prices were 242.4$ per night. In contrast, Melbourne’s prices remained relatively stable, with a slight increase from 197.7$ per night in 2019Q1 to 202.8$ in 2023Q3. Building on the data processing steps outlined above, we have derived the following transformed variables:

Figure 3.

Quarterly number of established Airbnbs in Sydney and Melbourne (2019Q1–2023Q3).

Figure 4.

Quarterly average Airbnb price in Sydney and Melbourne (2019Q1–2023Q3).

Airbnb Density, Avg Airbnb Price, Avg Airbnb Bedrooms, and % Airbnb Shared

“Airbnb Density” represents the number of Airbnbs per square kilometer in each SA2, each year/quarter. Similarly, “Avg Airbnb Price” denotes the average price of all Airbnbs in the SA2, “Avg Airbnb Bedrooms” indicates the average number of bedrooms in Airbnbs in a given SA2, and “% Airbnb Shared”⁵ represents the percentage of shared Airbnbs in each SA2.

PostPolicy

PostPolicy is a dummy variable which equals 1 if the year/quarter of a given observation is after the intervention time and 0 if before. We have set up the first year/quarter of the intervention time to be 2021Q2 when discussing our announcement date (April 9^th, 2021) and 2021Q4 when discussing implementation date (November 1^st, 2021).

Sydney and PostPolicy*Sydney

The variable ”Sydney” is a dummy variable equal to 1 if the SA2 is located in the Greater Sydney area and 0 if located in the Greater Melbourne area. The interaction term “PostPolicy*Sydney” represents our DiD estimator, which estimates the effect of the policy on rental prices.

Rents and number of rental bonds

We have incorporated rental data from the Department of Communities and Justice NSW (n.d) and the Department of Families Fairness and Housing Victoria (n.d). This data includes the median weekly rent and the number of rental bonds held each quarter at the LGA⁶ level. Since our analysis is conducted at the SA2 level, we adjust these LGA-level measures to ensure consistency in spatial aggregation. The following subsections describe the transformation process for rental prices and total bonds:

Weighted Avg Rent Price

The dependent variable “Weighted Avg Rent Price” represents the weekly rent in a given SA2 at a given year/quarter, calculated based on the rent in the LGA where the SA2 is located. If an SA2 spans multiple LGAs, this value is a weighted average of the rents from all the LGAs that the SA2 covers. The weights⁷ are determined by the proportion of each SA2 that the LGA occupies. For example, the SA2 of Camperdown is located 60% in the City of Sydney LGA and 40% in the Inner West LGA.

Weighted Total Bonds

“Weighted Total Bonds” represents the total number of rental bonds held by the NSW and Victoria government in each year/quarter in the LGA where the SA2 is located or the weighted value of the total bonds in all LGAs covered by the SA2 if there is more than one.

Schools, crime, and economic resources

As studies have shown, proximity to quality schools has a positive effect both on house prices (Doko Tchatoka and Varvaris, 2021; Lu et al., 2023) and rentals (Kuroda, 2018). To reflect this, we have included schooling data from the Australian Curriculum, Assessment and Reporting Authority (ACARA), using the Index of Community Socio-Educational Advantage (ICSEA) values. The ICSEA value represents the levels of educational advantage in each school in Australia, calculated based on student background factors (parents’ occupation and education) and school factors (geographical location and proportion of Indigenous students). The ICSEA scale has a median value of 1000 with a standard deviation of 100, typically ranging from 500, representing extremely educationally disadvantaged backgrounds, to about 1300, representing schools with students from very educationally advantaged backgrounds (Australian Curriculum, Assessment and Reporting Authority, 2013). In addition, we have added annual crime rate data from the NSW Bureau of Crime Statistics and Research (2024) (BOCSAR) and the Crime Statistics Agency Victoria (2024). This data, collected at the LGA level, includes crime rates per 100,000 people for assault, theft, and property damage. The crime rate is defined as the sum of these three crime types per 100,000 people. These specific crime types were chosen because they are among the most common in Australia (Australian Bureau of Statistics, 2023b). To account for broader economic conditions, we incorporate the Index of Economic Resources (IER) from the Australian Bureau of Statistics (2021e). The IER is a component of the Socio-Economic Indexes for Areas (SEIFA) which measures access to financial resources based on household income, housing costs, employment conditions, home ownership, and family structure. It is calculated at the SA2 level, with higher values indicating greater economic stability and lower values reflecting financial hardship. The index is standardized, with an average score of 1000 and a standard deviation of 100 across all areas in Australia. While the ABS does not specify exact minimum and maximum values, IER scores typically range between 600 and 1400 (Australian Bureau of Statistics, 2021a).

From this set of variables, we have derived the following transformations:

Weighted Avg Crime Rate, Avg Quality of Schooling, and IER

Weighted Avg Crime Rate⁸ represents the rate of crime per 100,000 people in the LGA where the SA2 is located or the weighted value if multiple LGAs involved. “Avg Quality of Schooling” is based on the average ICSEA value of all schools in a given SA2. The IER values have been converted into deciles, with lower deciles representing areas with fewer economic resources and higher deciles indicating greater economic advantage.

For the transformed variables discussed, Table 2 provides the corresponding descriptive statistics, including their mean, standard deviation, minimum, and maximum values.

SA2FE and TimeFE

Lastly, we have incorporated time and location fixed effects where “SA2FE” controls for unobserved, time-invariant characteristics within each SA2. This means that any characteristics specific to each SA2 that do not change over time are accounted for, isolating the impact of other explanatory variables. Conversely, “TimeFE” controls for any factors that affect all SA2s equally at a given year/quarter. This includes economic conditions, seasonal trends, COVID-19,⁹ and other temporal factors that could influence rental prices across both cities. By including time-fixed effects, we can ensure that the model accounts for broad time-related effects, isolating the specific impact of the policy change. The decision to use fixed effects was supported by a Hausman test, which assessed whether the unique errors (or individual effects) of each SA2 and temporal effects are correlated with the regressors. This test compares the fixed effects model with the random effects model to determine which is more appropriate (Hausman, 1978). A significant Hausman test result indicated that the fixed effects model is preferred, as it accounts for this correlation, thereby providing more consistent and unbiased estimates than the random effects model for our data.

All observations missing rental, schooling, or crime data were excluded from the study. Additionally, applying time and location fixed effects resulted in the removal of all observations (both Airbnb and non-Airbnb-related) that remained unchanged across all quarters of the study within the SA2. In Figure 5, we observe the average quarterly rents in Sydney and Melbourne. The data indicates that both cities experienced similar rent trends, with noticeable increases occurring after the time of the announcement and implementation of the 180-day limit of the STRA laws. Notably, rent prices in Sydney and Melbourne rose from 497$ and 402$ per week, respectively, in the pre-announcement period (2021Q1), to 664$ and 508$ per week by 2023Q3.

Figure 5.

Quarterly average rent prices in Sydney and Melbourne (2019Q1–2023Q3).

Empirical framework and regression model

To estimate the effectiveness of Airbnb laws on the impact of short-term listings on long-term rents, the research will be conducted using a DiD regression with fixed effects approach, which is widely recognized in the field of economics for studying causal relationships in quasi-experimental settings (Abadie, 2005; Athey and Imbens, 2022; Goodman-Bacon, 2021; Wooldridge, 2010). The DiD methodology involves comparing changes in outcomes over time between a treatment group and a control group before and after a specific intervention, which in this case is the announcement and implementation of the STRA Laws (Angrist and Pischke, 2009). By using the DiD method, the research aims to quantify the causal effect of this regulation on rental market dynamics, specifically on the relationship between short-term Airbnb listings and long-term rental prices. As previously mentioned, fixed effects will also be used to control for time and location invariant factors that may vary across SA2s in Sydney and Melbourne. The DiD framework inherently addresses potential endogeneity concerns by using a control group to account for broader economic and market trends, allowing for a more robust estimation of the policy’s impact (Angrist and Pischke, 2009). By using both treated (Sydney) and control (Melbourne) groups, DiD accounts for underlying differences that could influence the outcomes, resulting in a more accurate estimation of treatment effects (Card and Krueger, 1994). One of the primary advantages of DiD is its ability to analyze dynamic changes over time, making it suitable for evaluating the effects of policies, regulations, or interventions that unfold gradually and may have cumulative impacts (Lechner, 2011). However, a crucial part of the research design is satisfying the parallel trends assumption. This assumption requires that in the absence of an intervention, the difference in trends between the treatment and control group should be constant over time. If this assumption is violated, the estimates of the treatment effect may be biased (Bertrand et al., 2004; Imbens and Wooldridge, 2009). For this study, we establish our DiD model as follows:

\begin{align} \log ({WeightedAvgRentPrice}_{i t}) & = α + β_{1} ({PostPolicy}_{t} \times {Sydney}_{i}) + γ X_{i t} \\ + SA 2 FE + TimeFE + ϵ_{i t} \end{align}

(1)

where X_it is defined as

X_{i t} = (\begin{matrix} \log ({WeightedTotalBonds}_{i t}) \\ \log ({WeightedAvgCrimeRate}_{i t}) \\ \log ({AvgQualityofSchooling}_{i t}) \\ {AirbnbDensity}_{i t} \\ \log ({AvgAirbnbPrice}_{i t}) \\ \log ({AvgAirbnbBedrooms}_{i t}) \\ {%AirbnbShared}_{i t} \\ {IER}_{i t} \end{matrix})

To verify the parallel trends assumption, we employ an event study approach following Callaway and Sant’Anna (2021). This method allows us to analyze the trends in the outcome variable across different time periods relative to the policy intervention. By including multiple periods before and after the event in our analysis, we can visually and statistically examine whether the pre-treatment trends are parallel between the treatment group (Sydney) and the control group (Melbourne). Specifically, we estimate the coefficients for interaction terms between the treatment indicator and time dummies for each quarter. If the coefficients for the pre-treatment quarters (before 2021Q2) are close to zero and statistically insignificant, it suggests that the parallel trends assumption holds (Roth, 2022; Sun and Abraham, 2021). Our approach builds upon the accepted event study method (Koster et al., 2021) by incorporating interaction terms between control variables and time dummies to account for conditional parallel trends. This captures any time-varying effects of the control variables, providing a more robust analysis (Callaway and Sant’Anna, 2021). We further enhance the reliability of the event study by including only those control variables that have a significant effect on the dependent variable. Our event study model is established as follows.

\begin{align} \log ({WeightedAvgRent}_{i t}) & = α + \sum_{k = - n}^{j} β_{k} ({EventWindow}_{k} \times {Sydney}_{i}) \\ + \sum_{k = - n}^{j} γ_{k} ({EventWindow}_{k} \times X_{i t}^{+}) \\ + δ X_{i t}^{+} \\ + SA 2 FE + TimeFE + ϵ_{i t} \end{align}

(2)

where

X_{i t}^{+}

is defined as

X_{i t}^{+} = (\begin{matrix} \log ({WeightedTotalBonds}_{i t}) \cdot 1_{{p < α}} \\ \log ({WeightedAvgCrimeRate}_{i t}) \cdot 1_{{p < α}} \\ \log ({AvgQualityofSchooling}_{i t}) \cdot 1_{{p < α}} \\ {AirbnbDensity}_{i t} \cdot 1_{{p < α}} \\ \log ({AvgAirbnbPrice}_{i t}) \cdot 1_{{p < α}} \\ \log ({AvgAirbnbBedrooms}_{i t}) \cdot 1_{{p < α}} \\ % {AirbnbShared}_{i t} \cdot 1_{{p < α}} \\ {IER}_{i t} \cdot 1_{{p < α}} \end{matrix})

where

1_{{p < α}}

is an indicator function that equals 1 if the p-value of the respective variable is less than the significance level α (e.g., 0.05), and 0 otherwise.

The term $\sum_{k = - n}^{j} β_{k} ({EventWindow}_{k} \times {Sydney}_{i})$ represents the interaction between the event window and the treatment indicator for Sydney. It captures the differential effect of the policy intervention over time for Sydney compared to Melbourne. Here, k denotes each specific quarter within the event window, n represents the starting point of the event window before the policy, and j represents the endpoint of the event window after the policy. We have selected 2020Q2 as the reference category for the event study because it represents a period just preceding the major economic disruptions caused by COVID-19. Although Australia began experiencing the initial impacts of COVID-19 at the start of this quarter, with nationwide lockdowns, increased government support, and a fall in GDP toward the end of the quarter (Australian Bureau of Statistics, 2022a, b), the significant economic disruptions started materializing after this period, including the lag effect of increased unemployment rate (Gilfillan, 2020). Additionally, this quarter was relatively free from additional significant economic shocks, policy changes, or seasonal effects that might have influenced rental price differences between in Sydney or Melbourne, thereby providing a reliable baseline for comparison. Furthermore, 2020Q2 is sufficiently distant from 2021Q2 to minimize the risk of potential anticipatory effects while still being relevant for assessing trends leading up to the intervention. Following this, the term $\sum_{k = - n}^{j} γ_{k} (EventWindow k \times X_{i t}^{+})$ represents the interaction between the event window and the vector of all significant control variables $X_{i t}^{+}$ . This captures how the relationship between these control variables and the outcome variable changes over time, accounting for conditional parallel trends, while $δ X_{i t}^{+}$ captures the main effects of the significant control variables on the outcome variable, independent of the event window.

Another key consideration in policy analysis is the presence of HTEs, where the impact of an intervention varies across different subgroups (Imbens and Rubin, 2015). In this case, the effect of the STRA laws on rental prices may not be uniform across all SA2s, as economic conditions could influence the degree of market response. To examine this, we conduct a separate analysis where SA2s are grouped by IER decile and estimate the treatment effect within each decile group. This approach allows us to determine not only whether rental price changes post-announcement and implementation were more pronounced in economically advantaged or disadvantaged areas but also whether the direction of these changes varied across these areas.

Results

We present the results in Table 3 where the upper part of the table shows the effects of the variables with the intervention time set as the announcement date (April 9th, 2021), and the lower part set as the implementation date (November 1st, 2021). In Column 1, we discuss the simple linear regression model, which examines the policy’s effect on rental prices. Column 2 includes only non-Airbnb-related variables (Weighted Total Bonds, Average Crime Rate, and Quality of Schooling, IER). Column 3 incorporates only Airbnb-related variables (Airbnb Density, Average Airbnb Bedrooms, Average Airbnb Price, and Percentage of Shared Airbnbs). Finally, Column 4 presents the complete model, which includes both Airbnb-related and non-Airbnb-related variables. This separation into four parts allows us to conduct robustness checks by allowing us to observe whether the significance or the direction of the effects of different variables change when additional variables are incorporated into the model, ensuring that our findings are robust, reliable, and not sensitive to different model specifications (Lu and White, 2014).

Table 3.

DiD model results.

Independent variable	Announcement date
Independent variable	(1)	(2)	(3)	(4)
Constant	6.1518***	4.5454***	6.2222***	3.2894***
	(0.0008)	(0.5178)	(0.006)	(0.6916)
PostPolicy*Sydney	0.0351***	0.0261***	0.0495***	0.0355***
	(0.0029)	(0.0018)	(0.0026)	(0.0039)
Log (Weighted Total Bonds)		0.1579***		0.1854***
		(0.0146)		(0.0285)
Log (Avg Crime Rate)		−0.0438***		−0.0327***
		(0.0065)		(0.0116)
Log (Avg Quality of Schooling)		0.0512		0.1781*
		(0.0723)		(0.0912)
Airbnb Density			0.0017***	0.002***
			(0.0002)	(0.0002)
Avg Airbnb Bedrooms			−0.003	−0.0009
			(0.0011)	(0.0012)
Log (Avg Airbnb Price)			0.0029**	0.0028**
			(0.0012)	(0.0013)
% Shared Airbnbs			0.0014	0.0008
			(0.0022)	(0.0024)
IER		0.0032**		0.0164***
		(0.0014)		(0.0027)
Number of observations	12673	10657	4301	3790
Within R²	0.0968	0.1224	0.1776	0.1979

Independent variable	Implementation date
Independent variable	(1)	(2)	(3)	(4)
Constant	6.1513***	4.6259***	6.2289***	3.2231***
	(0.0005)	(0.5223)	(0.0061)	(0.7021)
PostPolicy*Sydney	0.038***	0.0305***	0.0436***	0.0273***
	(0.0022)	(0.0016)	(0.0026)	(0.0037)
Log (Weighted Total Bonds)		0.1561***		0.2098***
		(0.0141)		(0.029)
Log (Avg Crime Rate)		−0.0486***		−0.042***
		(0.0065)		(0.0117)
Log (Avg Quality of Schooling)		0.0471		0.1639*
		(0.0728)		(0.0927)
Airbnb Density			0.0017***	0.002***
			(0.0003)	(0.0002)
Avg Airbnb Bedrooms			−0.0003	−0.0008
			(0.0012)	(0.0012)
Log (Avg Airbnb Price)			0.0023*	0.0023*
			(0.0012)	(0.0013)
% Shared Airbnbs			0.0009	0.0006
			(0.0022)	(0.0024)
IER		0.003**		0.0156***
		(0.0013)		(0.0027)
Number of observations	12673	10657	4301	3790
Within R²	0.1383	0.1639	0.1991	0.2146

Note: The dependent variable is Log (Weighted Avg Rent). Robust standard errors are shown in parentheses. All specifications include SA2 fixed effects and Year-Quarter fixed effects.

*p $<$ 0.1, **p $<$ 0.05, ***p $<$ 0.01.

The results of the complete model (4) show a significant positive effect of the STRA laws, with rental prices increasing by 3.55% after the announcement and by 2.73% after implementation. For the natural logarithm of weighted total bonds, the coefficient is at a significant 0.185% and 0.21% for the announcement and implementation dates, respectively. It is worth adding that since the total number of bonds in each SA2 observation represents the number of bonds in the corresponding LGA (or the weighted total bonds if the SA2 spans multiple LGAs), the isolated effect of a single SA2 can be calculated as the logarithm of the weighted total bonds of the LGA divided by the average number of SA2s inside the LGA, assuming normal distribution. Given that there is an average of 10.35 SA2s per LGA in Greater Sydney and Melbourne, a 1% increase in the total number of bonds in a single SA2 would correspondingly elevate the average rental prices by 0.0178% and 0.02% for announcement and implementation date, respectively. Regarding the average crime rate, since this variable represents an average rate of all SA2s within each LGA and assuming a normal distribution, an increase in crime rates in an SA2 is associated with a 0.0327% reduction in rental prices post-announcement and a 0.042% reduction post-implementation. Additionally, each additional Airbnb per square kilometer (“Airbnb Density”) significantly raises rental prices by 0.002% post-announcement and implementation. However, the log of average Airbnb prices is only significant at the announcement stage, where a 1% increase in average Airbnb prices is associated with a 0.0028% rise in rental prices. This suggests that initial expectations regarding Airbnb pricing may have influenced rental markets immediately after the policy was announced, but this effect dissipated at the time of implementation. Furthermore, the IER demonstrates a positive and significant effect on rental prices, with coefficients of 0.0164% post-announcement and 0.0156% post-implementation which indicates that rental prices and their subsequent increases tend to be higher in areas with greater economic resources. Meanwhile, no significant effects on rental prices were found for the average quality of schooling, the average number of bedrooms in Airbnbs, or the percentage of shared Airbnbs in each SA2. Robustness checks across all four models demonstrate that key variables have consistently remained significant at the p $<$ 0.05 level, while variables that were insignificant remained consistently so. Additionally, there were relatively small variations in the coefficients and standard error values for all variables across all four models. As such, this consistency and stability in both significant and insignificant variables suggest the reliability and robustness of our results. Furthermore, all models with more than one explanatory variable (Columns 2–4) were assessed using a variance inflation factor (VIF) test. The VIF test measures how much the variance of a regression coefficient is inflated due to multicollinearity among the explanatory variables. The results indicated no evidence of multicollinearity among the variables in all models (Appendix Table 5).

This outcome indicates that the policy has not only failed to reduce rental prices by limiting Airbnb activity but has actually contributed to additional rental price increases. One possible explanation is that short-term rental investors continue to prefer holding their properties for short-term rentals and compensate for the expected losses from the new policy by raising the prices of their Airbnbs and establishing new Airbnbs. This may indicate a preference for the more risky (due to the limited potential occupancy timeframe) but potentially more rewarding short-term rental market over the more stable but less lucrative long-term rental market. This intuition is supported by the continuing increase in the establishment of new Airbnbs, particularly after the policy was implemented (Figure 3), as well as the continuous rise in average Airbnb prices in Sydney, with some fluctuations around the announcement and implementation dates, but an overall upward trend since 2019Q1 (Figure 4).

In Figure 6, we present the event study on the effect of the STRA laws on rental prices in different quarters before and after the policy intervention as part of our effort to verify the parallel trends assumption. It is important to note that since we only incorporate control variables that have a significant effect on the dependent variable in relation to the announcement date, the variables “Avg Quality of Schooling,” “Avg Airbnb Bedrooms,” and “% Shared Airbnbs” were excluded from the event study. The coefficients for the quarters leading up to the policy announcement (pre-2021Q2) are close to zero and not statistically significant, suggesting that the rental price trends between Sydney and Melbourne were parallel before the policy intervention, thus satisfying the parallel trends assumption. Post-intervention, the coefficients become positive and significant, indicating an increase in rental prices following the announcement and implementation of the STRA laws indicating the policy’s impact on rental prices with the positive coefficients post-intervention showing that rental prices in Sydney increased more than those in Melbourne, suggesting a significant effect of the STRA laws on rental prices. In addition to the visual representation in Figure 6, Appendix Table 6 provides a detailed summary of all estimated coefficients with their respective leads and lags, standard errors, and statistical significance.

Figure 6.

Event study. Note: The blue points indicate the estimated coefficients, and the vertical lines represent the 95% confidence intervals. The dashed horizontal line indicates the 2020Q2 baseline (zero effect) for reference.

As part of our HTE analysis, we classify SA2s by IER, grouping areas with an IER above 5 (economically advantaged) separately from those with an IER of 5 or below (economically disadvantaged), to assess whether the impact of the STRA laws on rental prices differs between economic levels. This analysis is conducted for both the announcement and implementation stages of the policy, with the results presented in Table 4.

Table 4.

Heterogeneous treatment effects by IER.

Independent variable	Announcement date
Independent variable	(IER $>$ 5)	(IER ≤5)
Constant	4.9343***	3.6173***
	(1.3987)	(0.7952)
PostPolicy*Sydney	0.0195***	0.0499***
	(0.0048)	(0.0056)
Log (Weighted Total Bonds)	0.178***	0.2025***
	(0.0368)	(0.0285)
Log (Avg Crime Rate)	−0.0184	−0.0508**
	(0.0137)	(0.02)
Log (Avg Quality of Schooling)	−0.0559	0.1205
	(0.1976)	(0.0986)
Airbnb Density	0.0022***	0.0015***
	(0.0008)	(0.0002)
Avg Airbnb Bedrooms	<−0.0001	−0.0027*
	(0.0012)	(0.0016)
Log (Avg Airbnb Price)	0.0032**	0.0029
	(0.0016)	(0.002)
% Shared Airbnbs	0.0031	−0.0012
	(0.003)	(0.0035)
IER	0.0144***	0.0205***
	(0.0044)	(0.0043)
Number of observations	2056	1734
Within R²	0.1372	0.2137

Independent variable	Implementation date
Independent variable	(IER $>$ 5)	(IER ≤5)
Constant	5.05***	3.5219***
	(1.377)	(0.8172)
Post policy*Sydney	0.0267***	0.0309***
	(0.0045)	(0.0053)
Log (Weighted Total Bonds)	0.1611***	0.2583***
	(0.0344)	(0.0407)
Log (Avg Crime Rate)	−0.0102	−0.0802***
	(0.0138)	(0.0205)
Log (Avg Quality of Schooling)	−0.0567	0.0862
	(0.1943)	(0.102)
Airbnb Density	0.002***	0.0014***
	(0.0007)	(0.0003)
Avg Airbnb Bedrooms	<-0.0001	−0.0027*
	(0.0012)	(0.0016)
Log (Avg Airbnb Price)	0.0029*	0.0023
	(0.0015)	(0.002)
% Shared Airbnbs	0.0031	−0.0008
	(0.003)	(0.002)
IER	0.0145***	0.018***
	(0.0044)	(0.0043)
Number of observations	2056	1734
Within R²	0.1766	0.2158

Note: The dependent variable is Log (weighted avg rent). Robust standard errors are shown in parentheses. All specifications include SA2 fixed effects and Year-Quarter fixed effects.

*p $<$ 0.1, **p $<$ 0.05, ***p $<$ 0.01.

Consistent with our main DiD model, the results show that the STRA laws led to an increase in rental prices across both economically advantaged (IER $>$ 5) and disadvantaged (IER ≤5) areas. The estimated treatment effects for both the announcement and implementation dates are positive and statistically significant, confirming that the policy had a widespread upward impact on rental prices. At the announcement stage, rental prices increased by 4.99% in lower IER areas and 1.95% in higher IER areas, suggesting that the initial market reaction was stronger in economically disadvantaged regions. This could be due to a greater reliance on short-term rentals as a secondary income source in these areas, leading to a sharper immediate response in the rental market as landlords adjusted to the new restrictions either by remaining in the short-term market and raising prices to compensate for the reduced booking period, thereby restricting long-term rental supply, or by shifting to long-term leasing but setting higher rents to recover losses from short-term constraints, both of which contribute to rising long-term rental prices. By the implementation stage, rental prices increased by 3.09% in lower IER areas and 2.67% in higher IER areas, indicating a more uniform response to the policy. This consistency across economic segments suggests that the STRA laws contributed to higher rental prices regardless of initial economic conditions, pointing to a broad market-wide response (Horn and Merante, 2017) rather than an effect concentrated in specific economic groups.

Conclusion

This study investigates the impact of Airbnb regulations on long-term rental prices in Sydney, with a specific focus on the effects of the STRA laws. Utilizing a DiD methodology, we compare rental prices in Sydney with those in Melbourne, serving as a control group, to isolate the effects of the STRA regulations. Our analysis reveals a significant increase in long-term rental prices by 3.55% and 2.73% in Sydney post-announcement and implementation of the STRA laws. This suggests that the restriction of short-term rentals to 180 days per year did not increase the supply of long-term rentals nor enhance their affordability as intended. Moreover, these regulations did not reduce the appeal of short-term rentals, resulting in a further tightening of the rental market and driving up prices. Beyond the effects of the STRA laws, our analysis also revealed significant impacts of other factors on long-term rental prices. An increase in the total number of rental bonds or Airbnb density in an SA2 is associated with higher rental prices, whereas higher crime rates are linked to lower rental prices.

Our findings contribute to the growing body of literature on the economic impacts of short-term rental regulations, particularly within the Australian market. Additionally, to verify the parallel trends assumption of our DiD model, we have employed a novel event study approach that accounts for conditional parallel trends by incorporating interaction terms between control variables and time dummies. Future research should explore and evaluate alternative regulatory frameworks such as designating specific zones for Airbnbs, limiting the number of occupants per Airbnb, or implementing a registry system that caps the number of operational Airbnbs in a city. Additionally, incorporating novel statistical methodologies such as machine learning models could enhance the prediction of the effects of different Airbnb policy types on the housing market.

While the STRA laws aim to address negative externalities associated with Airbnb, such as neighborhood disruption and reduced housing availability, they may inadvertently exacerbate rental market tightness. Policymakers need to consider these trade-offs to prevent unintended consequences that could negatively affect housing affordability, finding a balance between leveraging the benefits of the sharing economy while safeguarding the availability of affordable long-term rental housing.

Footnotes

Author contributions

Alexei Roudnitski: Conceptualization, Methodology, Formal analysis, Investigation, Writing—Original Draft, and Visualization. Somwrita Sarkar: Conceptualization, Investigation, Resources, Writing—Review and Editing, and Supervision.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Alexei Roudnitski

Somwrita Sarkar

Data Availability Statement

These data were derived from the following resources available in the public domain: https://insideairbnb.com/. https://dcj.nsw.gov.au/about-us/families-and-communities-statistics/housing-rent-and-sales/. https://rent-and-sales-report.html. https://www.dffh.vic.gov.au/publications/rental-report. https://www.crimestatistics.vic.gov.au/crime-statistics/latest-crime-data-by-area. https://www.bocsar.nsw.gov.au/Pages/bocsar_crime_stats/bocsar_crime_stats.aspx. https://www.acara.edu.au/contact-us/acara-data-access. https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/correspondences.

Notes

Appendix

Table 5.

Variance inflation factors (VIFs).

Variable	(2)	(3)	(4)
Announcement date
Post policy*Sydney	1.037	1.05	1.133
Log (weighted total bonds)	1.63		1.857
Log (avg crime rate)	1.814		2.45
Log (avg quality of schooling)	1.18		1.18
Airbnb density		1.041	1.223
Avg Airbnb bedrooms		1.423	1.471
Log (avg Airbnb price)		1.84	1.891
% shared Airbnbs		1.698	1.747
IER	1.56		1.807
Implementation date
Post policy*Sydney	1.026	1.063	1.14
Log (weighted total bonds)	1.614		1.842
Log (avg crime rate)	1.802		2.437
Log (avg quality of schooling)	1.118		1.186
Airbnb density		1.041	1.224
Avg Airbnb bedrooms		1.42	1.466
Log (avg Airbnb price)		1.857	1.911
% shared Airbnbs		1.693	1.742
IER	1.56		1.807

Note: A VIF above 5 indicates a high possibility of multicollinearity.

Table 6.

Event study results.

Variable	Coefficient	Std. Err
Const	2.8776***	(0.5173)
Log weighted total bonds	0.3511***	(0.0580)
Log weighted average crime rate	0.0564***	(0.0213)
Airbnb density	−0.0012	(0.0030)
Log avg Airbnb price	0.0006	(0.0050)
IER	0.0042	(0.004)
Sydney_t−3	−0.0134	(0.0120)
Sydney_t−2	−0.0217*	(0.0132)
Sydney_t−1	0.0081	(0.0117)
Sydney_t0	0.0317**	(0.0123)
Sydney_t+1	0.0487***	(0.0134)
Sydney_t+2	0.0294**	(0.0137)
Sydney_t+3	0.0392***	(0.0111)
Sydney_t+4	0.0453***	(0.0106)
Sydney_t+5	0.0502***	(0.0104)
Sydney_t+6	0.0541***	(0.0107)
Sydney_t+7	0.0672***	(0.0112)
Sydney_t+8	0.0490***	(0.0113)
Sydney_t+9	0.0512***	(0.0109)
Log weighted total bonds_t−3	−0.0162	(0.0171)
Log weighted total bonds_t−2	−0.0198	(0.0188)
Log weighted total bonds_t−1	−0.0426**	(0.0168)
Log weighted total bonds_t0	−0.0351**	(0.0169)
Log weighted total bonds_t+1	−0.0506***	(0.0177)
Log weighted total bonds_t+2	−0.0574***	(0.0191)
Log weighted total bonds_t+3	−0.0393**	(0.0164)
Log weighted total bonds_t+4	−0.0367**	(0.0159)
Log weighted total bonds_t+5	−0.0424***	(0.0157)
Log weighted total bonds_t+6	−0.0445***	(0.0159)
Log weighted total bonds_t+7	−0.0602***	(0.0161)
Log weighted total bonds_t+8	−0.0292*	(0.0163)
Log weighted total bonds_t+9	−0.0291*	(0.0158)
Log weighted average crime rate_t−3	−0.0131	(0.0183)
Log weighted average crime rate_t−2	−0.0280	(0.0194)
Log weighted average crime rate_t−1	−0.0231	(0.0171)
Log weighted average crime rate_t0	−0.0462**	(0.0181)
Log weighted average crime rate_t+1	−0.0486**	(0.0190)
Log weighted average crime rate_t+2	−0.0235	(0.0220)
Log weighted average crime rate_t+3	−0.0296*	(0.0164)
Log weighted average crime rate_t+4	−0.0251	(0.0158)
Log weighted average crime rate_t+5	−0.0114	(0.0157)
Log weighted average crime rate_t+6	−0.0146	(0.0157)
Log weighted average crime rate_t+7	0.0111	(0.0160)
Log weighted average crime rate_t+8	−0.0362**	(0.0160)
Log weighted average crime rate_t+9	−0.0258*	(0.0154)
Airbnb density_t−3	0.0044	(0.0050)
Airbnb density_t−2	0.0025	(0.0053)
Airbnb density_t−1	−0.0009	(0.0049)
Airbnb density_t0	0.0055	(0.0043)
Airbnb density_t+1	0.0031	(0.0071)
Airbnb density_t+2	−0.0003	(0.0091)
Airbnb density_t+3	0.0007	(0.0040)
Airbnb density_t+4	0.0036	(0.0036)
Airbnb density_t+5	0.0042	(0.0034)
Airbnb density_t+6	0.006*	(0.0032)
Airbnb density_t+7	0.0026	(0.0029)
Airbnb density_t+8	0.0029	(0.0030)
Airbnb density_t+9	0.0015	(0.0029)
Log avg Airbnb price_t + 9−3	0.0013	(0.0064)
Log avg Airbnb price_t + 9−2	0.0011	(0.0068)
Log avg Airbnb price_t + 9−1	0.0044	(0.0064)
Log avg Airbnb price_t + 90	0.0072	(0.0064)
Log avg Airbnb price_t + 9 + 1	0.0125	(0.0079)
Log avg Airbnb price_t + 9 + 2	0.0047	(0.0064)
Log avg Airbnb price_t + 9 + 3	0.0078	(0.0060)
Log avg Airbnb price_t + 9 + 4	−0.0013	(0.0057)
Log avg Airbnb price_t + 9 + 5	−0.0056	(0.0055)
Log avg Airbnb price_t + 9 + 6	−0.0022	(0.0055)
Log avg Airbnb price_t + 9 + 7	0.0076	(0.0055)
Log avg Airbnb price_t + 9 + 8	−0.0053	(0.0060)
Log avg Airbnb price_t + 9	−0.0098*	(0.0057)
IER_t−3	0.0031	(0.0022)
IER_t−2	0.0073***	(0.0023)
IER_t−1	0.0054***	(0.0021)
IER_t0	0.0063***	(0.0020)
IER_t+1	0.0052**	(0.0024)
IER_t+2	0.0078***	(0.0028)
IER_t+3	0.0040**	(0.0019)
IER_t+4	0.0019	(0.0017)
IER_t+5	0.0018	(0.0017)
IER_t+6	0.0027*	(0.0016)
IER_t+7	0.0001	(0.0017)
IER_t+8	−0.0002	(0.0016)
IER_t+9	−0.0009	(0.0016)

Note: The dependent variable is Log (Weighted Avg Rent). Robust standard errors are shown in parentheses. All specifications include SA2 fixed effects and year-quarter fixed effects.

*p $<$ 0.1, **p $<$ 0.05, ***p $<$ 0.01.

Alexei Roudnitski is a PhD candidate at the School of Architecture, Design, and Planning, University of Sydney. His research focuses on housing and transportation planning, with a particular emphasis on urban economics. He investigates the impacts of external policy shocks on urban infrastructure pricing and costs, utilizing spatial analysis and econometric methods to inform how regulatory changes shape city dynamics.

Somwrita Sarkar is an Associate Professor at the School of Architecture, Design, and Planning at the University of Sydney. Her research explores cities as complex systems, focusing on how urban physical structures both influence and are shaped by socio-economic inequalities. By developing and applying methods from spatial science, complex systems theory, network analysis, geography, economics, and physics, her work informs urban planning and policy, particularly in the areas of sustainable and equitable city development.

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The effect of policy regulations in the short-term rental platform market on long-term rental prices: A case study of Airbnb in Sydney

Abstract

Keywords

Introduction

Background: Case study—Sydney

Paper contribution

Data and descriptives

Airbnb and the STRA Policy

Airbnb Density, Avg Airbnb Price, Avg Airbnb Bedrooms, and % Airbnb Shared

PostPolicy

Sydney and PostPolicy*Sydney

Rents and number of rental bonds

Weighted Avg Rent Price

Weighted Total Bonds

Schools, crime, and economic resources

Weighted Avg Crime Rate, Avg Quality of Schooling, and IER

SA2FE and TimeFE

Empirical framework and regression model

Results

Conclusion

Footnotes

Author contributions

Declaration of conflicting interests

Funding

ORCID iDs

Data Availability Statement

Notes

Appendix

References