The impact of airport noise on house prices.

Using individual house sale data and the release of an airport noise contour map, the relationship between airport noise contours and house prices in Melbourne is studied. House prices are not related to noise contours but annual aircraft movements and distance from the runway are both identified as important determinants of the price of houses near the airport. Houses further from the airport runway but with the same observable characteristics command a premium of up to 37% which is likely attributable to a range of externalities from the nearby airport, including airport noise. Results are robust to tests for time variation in airport disamenity values. The results suggest households use distance to the runway as a proxy for the effects of airport noise.

). In the real estate domain, the issue of information disclosure is studied for example by Gatzlaff et al. (2017) (hurricane mitigation) and Pope (2008) (airport noise). For most people, their home is a large investment and comprises most if not all their wealth. Information on the local amenity or disamenity of a potential home purchase can have major welfare effects.
In this paper we empirically study how information from publicly available noise contour maps are incorporated into prices paid by homebuyers. The context is Essendon airport, 10 km northwest of the central business district in Melbourne, Australia, a domestic airport with, on average, 150 daily aircraft movements. Using data on individual house sales between 1998 and 2013, along with individual property and suburb characteristics, we estimate a hedonic pricing model for properties that might be affected by noise from Essendon airport. We first consider a model that incorporates information on each property's location on the 2008 Australian Noise Exposure Forecast (ANEF) map of the area surrounding the airport, along with travel distance to the airport, annual aircraft movements and other property related factors. Importantly, the 2008 ANEF map was the first Essendon airport noise contour map that was widely disseminated on the internet through its inclusion in the 2008 Essendon Airport Master Plan (Essendon Airport, 2008). 1 We use the release of this map as a form of exogenous variation in the availability of airport noise information regarding Essendon airport which allows us to test for changes in the relationship between house price and noise contours after the release of the map. The key results are that house prices are uncorrelated with ANEF contours and that the relationship between price and ANEF contours does not contour information is uncorrelated with house prices, it may be the case that it is too specialised or difficult to access or use for most homebuyers to incorporate into their home buying decisions or that airport noise as reflected by the ANEF map is not a major concern for homebuyers.
Using straight line distance from the airport runway as an alternative measure of airport disamenity, we find a positive but nonlinear relationship between house price and distance to the runway while the relationship with annual aircraft movements is maintained. House sale prices rise with the distance from the runway after controlling for a range of property and neighbourhood characteristics. Compared to the homes closest to the runway (0.25 km), the maximum price premium is found to be 37 percent at 1.64 km from the runway while the marginal effect of a reduction in annual aircraft movements of 1000 is 4.5 percent (average aircraft movements is around 62000 for the period studied). At a distance of 1.64 km, most houses are outside the outer noise contour (ANEF 20), but we can find many houses between the ANEF 20 and ANEF 25 noise contours. However, we argue part of the explanation for noise contours being uncorrelated with house price is that many houses in this area are much closer to the runway. A test of the price -distance relationship in this area confirms it is robust. Our results suggest, at such short distances, homebuyers might be inclined to believe that they will be affected by the airport because of its proximity, irrespective of the property's position on an ANEF map.
Recent studies by Lawton and Fujiwara (2016) and Ozkurt et al. (2015) reiterate the negative effects of airport noise on nearby residents. It is natural that changes in the externalities of airports such as noise would lead to changes in house prices. 2 It is also the case that houses in different locations relative to the airport will be more and less affected by airport noise as will the purchase price of those houses. Numerous studies have shown the negative effects of airport noise on house prices. Nelson (2004) provides a meta-analysis of the literature, showing discounts of between 0.50-0.90 percent per decibel (dB) increase in noise.
In a study of the area around Amsterdam airport, Dekkers and van der Straaten (2009) find discounts, relative to unaffected houses, of up to 13 percent for houses experiencing aircraft noise in the 50-55 dB range, McMillen (2004) finds a 9 percent discount for houses exposed to 65 dB or more airport noise around Chicago O'Hare airport, while Cohen and Coughlin (2008) find a 20.8 percent discount for houses exposed to more than 70 dB of aircraft noise in the area around Atlanta airport in the U.S. 3 Using the closure of Hellenikon international airport in Athens, Greece, Thanos et al. (2015) find a nonlinear relationship, consistent with the logarithmic nature of the dB scale, between house price and airport noise, with discounts of 0.40 and 2.38 percent per dB for houses exposed to noise exceeding 45 and 75 dB respectively. Our finding of a non-linear relationship between house price and distance to the runway provides some support for the claim that distance may proxy for the effects of airport noise on house price.
The result that house prices are uncorrelated with ANEF contours but are correlated with straight line distance to the runway has at least two explanations. First, relative to airport noise, homebuyers may be more concerned with air pollution, road congestion, visual presence and other possible disamenities of the airport that might be correlated with distance to the runway. A second explanation is that of imperfect information in this real estate market where buyers are either unaware of the specialised noise contour information or struggle to find or incorporate the information into their decision. This relates to the work of Pope (2008) who shows the price discount on airport noise affected properties increased from 7.8 percent to 10.7 percent after the introduction of mandatory disclosure laws in North Carolina in 1996, implying that homebuyers did not fully incorporate the effects of airport noise into their buying decisions and ultimately the home price. Given this important result and the large literature that points to the negative impact of airport noise on house price we focus on this latter interpretation, where buyers are likely to care about potential airport noise, along with other airport externalities, and take a rule of thumb approach, focusing on the straight line distance to the runway as a proxy for both noise and other effects of the nearby airport.
In the next section we provide some key details of Essendon airport and the surrounding area.
In Section 3 we describe the noise contour and distance data that are the focus of our analysis.
We also explain the house price and characteristics data used, providing summary statistics.
The empirical approach, results and a series of robustness tests are presented in Section 4 with conclusions presented in Section 5.

Essendon airport background
Essendon airport is located 10 km north-west of the Melbourne central business district (CBD) in Victoria. It was set up in 1919 and became Australia's second international airport in 1950. At that time, it was Melbourne's only airport. However, as air travel grew in popularity, the airport was not suitable for larger aircraft such as the Boeing 707 and an alternative was developed. In 1970, international flights were routed away from Essendon airport and instead through Melbourne Airport, 19km north-west of the Melbourne CBD. 4 The period for which we study the impact of the airport on house prices, 1998-2013, is long after international flights ceased. Over this period, Essendon airport served as a domestic airport, hosting regular passenger transport to regional locations, charter and tourist flights, emergency services flights (including helicopters), freight and private aircraft. Figure 1 shows the number of aircraft movements has declined gradually since 1999. Some variability in movements is a result of the airport's emergency services role which includes Victorian Police Air Wing and Air Ambulance services; e.g. the 6% increase in movements in 2009 is related to the major Victorian bushfires in that year and the resultant increase in emergency services use of the airport. All these services combine to over 50,000 aircraft movements per annum, or close to 150 movements per day on average. 5 As a consequence, Essendon airport has the potential for significant impact on the amenity of nearby residents.
As can be seen from Error! Reference source not found., the airport comprises two runways. Runway 17/35 is a 1.5 km north-south oriented runway while runway 08/26 is a 1.9 km east-west oriented runway. This study focuses on the suburbs to the south of runway These properties are also separated from the airport by a freeway. To the south of the airport, the freeway has a lower elevation than neighbouring suburbs which are protected by high walls (or noise barriers) serving to minimise the noise effects of the freeway.
Notwithstanding this, we anticipate continuous freeway noise will have a qualitatively different effect to intermittent airport noise and thus control for proximity to the freeway in our analysis with an indicator variable defined below; see Andersson et al. (2010) for a similar example, distinguishing rail and freeway noise.

Data
To study the impact of airport and aircraft noise on house prices, our analysis brings together data on house sales and characteristics, airport noise contours, distance from runway and neighbourhood characteristics. We explain these data in turn below.

Impact of the airport
Our approach to measuring the impact of the airport on surrounding suburbs is to use publicly available ANEF maps. These contour maps classify areas surrounding airports according to the extent and intensity of aircraft noise exposure. The ANEF is similar to the Noise Exposure Forecast (NEF) used internationally. One key difference is that higher impact night time noise is evaluated between 7pm-7am with the ANEF whereas it is evaluated between 10pm-7am with the NEF. 7 ANEF contour maps are typically prepared by airport owners or operators and endorsed by Airservices Australia, an Australian government owned corporation. These contour maps are included in the master plan of each major airport and are used by local government authorities for planning purposes. The contours take into account forecast changes in aircraft movements, audible frequencies of those movements and the daily distribution of movements with night time movements multiplied by a factor of four; see Essendon Airport (2013, pg. We complement the noise contour data described above with the straight line distance to the runway as an alternative measure of disamenity due to airport proximity. We consider this alternative measure because of its potential to capture other aspects of airport disamenity such as air pollution and visual presence, in addition to noise effects. The straight line distance also offers a simple rule of thumb measure that may be used by homebuyers who may not understand or be aware of the more sophisticated noise contour maps available to the public. It is consistent with the hedonic pricing literature on the value of nearby amenities such as schools, public transport facilities or parks; see for example Song and Knapp (2003) and Tyrväinen and Miettinen (2000) for valuing amenities and Michaels and Smith (1990) for disamenities. It was measured using Google Maps. The measurement was automated in Microsoft Excel and calculated using the latitude and longitude of both the southern end of runway 17/35 (denoted as point R in Error! Reference source not found.) and the address of each property sold (each grey dot in Error! Reference source not found.).
Linear distance to the airport entrance or terminal is typically used in the airport disamenity literature to capture airport access which is a measure of positive aspects of airport proximity such as convenience of access and nearby employment opportunities; Cohen and Coughlin (2008), Pope (2008), Tomkins et al. (1998) all find airport access adds value to a property, though Espey and Lopez (2000) find the opposite. However, Essendon airport is separated from affected suburbs by a freeway. As a consequence, the shortest car travel distance differs from the often used straight line distance to the terminal. The former is also measured using Google Maps and Microsoft Excel, calculating the shortest road travel distance between the addresses of the Essendon airport terminal (denoted as point T in Error! Reference source not found.) and each property sold (each grey dot in Error! Reference source not found.).

House prices and characteristics
House prices are based on data for all sales in the area under study from 1 January 1998 to 30 influence house value; see for example Black (1999). 11 Access to public transport is another important local amenity. The properties being studied are located close to the Craigieburn train line, providing access to the Melbourne CBD within 16-23 minutes during peak hour.
For each property, we compute and include the distance to the nearest train station to control for any benefits (or costs) arising from proximity to these stations.
In addition to these local neighbourhood characteristics, we also control for proximity to the freeway with an indicator for all houses within 200 meters of the freeway; a similar approach is used by Andersson et al. (2010) when distinguishing road noise from a nearby motorway and intermittent railway noise. Despite efforts to minimise the impacts of freeway noise, we anticipate proximity to the freeway to have a negative effect on house price. We also present in Table 1 summary statistics by noise contour areas to illustrate differences between the different areas around the airport. The first pair of columns present means and standard deviations for houses between the ANEF 25 and 30 contours. These are the sample houses that will experience the greatest level of airport noise. The second pair of columns present these statistics for houses between the ANEF 20 and 25 contours while the third pair of columns present characteristics of houses in our sample that lie outside the ANEF 20

Summary statistics
contour. Houses will be exposed to less extreme airport noise as we move to lower ANEF areas, while houses in the "Outside ANEF 20" column of Table 1 should experience acceptable levels of airport noise from Essendon airport.
Average prices rise from $515,000 to $810,000 as we move from ANEF 25-30 to ANEF 20-25 but fall back to $689,000 outside the ANEF 20 contour. These differences may be related to block size and built area which are on average highest in the ANEF 20-25 area, however, the price pattern persists after controlling for these characteristics in the noise contour modelling below. Other notable features are that average straight line distance is greater in the lower ANEF areas, but travel distance is similar between the ANEF 20-25 and outside the ANEF 20 contours but is lower for properties between the ANEF 25-30 contours. The freeway is closest to properties between the ANEF 25-30 contours with 50 percent within 200 meters. This fraction falls to 8 percent for houses in the ANEF 20-25 area and 17 percent of houses outside the ANEF 20 contour.

Methodology and results
We estimate a hedonic price model that incorporates both individual property and locational characteristics. The key idea of this approach is that house values are a composite of these various characteristics. 12 In our case, one of these characteristics is location relative to the airport. The baseline model estimated incorporates noise contour information and is given by: where , is the price that house i sold for in period t, and are indicators of a property's position in the ANEF 20-25 area and outside the ANEF 20 contour respectively, with the omitted category being properties within the ANEF 25-30 area. A range of property characteristics such as travel distance to the airport, log( ), log( ), construction type (weatherboard/brick), type of house (free-standing/semi-detached), number of bedrooms, freeway proximity and distance to nearest train station are included in , . We also include a suburb indicator which serves as a suburb fixed effect, to capture variation in suburb specific characteristics and amenities. 13 An indicator for local primary school is included to capture the impact on house prices of such sought after local amenities. While we have controlled for inflation, we expect that other aggregate factors that vary over the business cycle such as interest rates, economic growth and unemployment to affect prices over time. To account for this, quarterly time fixed effects ( ) are included, while the disturbance term , is assumed to have zero mean and constant variance.
The model is also estimated with the noise contour information replaced with distance to the runway as follows: where measures airport disamenity by the straight line distance from property i to runway 17/35 and is included as a second order polynomial. Other variables are as already defined while , is a zero mean, constant variance error term.
The models in (1) and (2) (1) and (2) with the inclusion of annual aircraft movements at Essendon airport. The approach is to include annual aircraft movements as an additional control and to consider the interaction of this variable with the noise contour variables and distance variables to test if the impact of airport noise on house prices evolves with annual aircraft movements. The inclusion of this additional variable focuses on an important mechanism through which the airport might affect property values; greater annual movements are likely to lead to greater noise effects on properties near the airport. Importantly, this additional analysis takes advantage of the temporal variation in annual aircraft movements to identify and test the stability of the relationship between property location, given by ANEF area or distance to the runway, and house price which might otherwise be captured by the included quarterly time fixed effects.
We further extend our empirical identification strategy by taking advantage of the release of the 2008 Essendon airport master plan. As location on the ANEF contour map is expected to be more strongly correlated with house price after the publication of the 2008 master plan, we construct an indicator for sales after the release of the master plan and interact it with the included noise contour indicators. The model estimated is given by: log P , = γ + γ N + γ N + γ N Post + γ N Post where the variable is an indicator of whether property i was sold after the ANEF contour map was released in 2008. Other variables are as defined above and , is the standard disturbance term. In this specification, compares the sale price of properties in the ANEF 20-25 area to those in the ANEF 25-30 area before the release of the ANEF contour map, while ( + ) reflects the same comparison after the release of the map. The parameter then reflects the difference in these differences and provides a test of the impact of the release of the information contained in the 2008 ANEF contour map on house prices.
The parameter provides a similar comparison for properties outside the ANEF 20 contour with those in the ANEF 25-30 area. The model in equation (3) is also extended through the inclusion of annual aircraft movements interacted with noise contour area indicators both before and after the release of the 2008 Essendon airport master plan, adding the temporal variation in movements to strengthen the identification of the effect of the release of the master plan.

Results
Parameter estimates for several alternative specifications of the models incorporating noise contours are presented in Table 2, with standard errors clustered by street in parentheses.
Models (1) and (3) in Table 2 are versions of the specification in equation (1) and include a range of property characteristics, suburb, freeway and local public school indicators, distance to the closest train station and time fixed effects, with model (3) adding annual aircraft movements along with interactions with noise contour indicators. 14 Models (2) and (4) in Table 2 are versions of the specification in equation (3), adding to models (1) and (3) respectively interactions between the noise contour variables (including those interacted with movements) and an indicator for sales occurring after 2008 when the master plan was made publicly available. Each of these specifications provide qualitatively similar results and a statistically strong explanation for house prices with R 2 ranging between 0.701 and 0.703. 14 A log-linear specification of airport access and distance to nearest train station was considered for models (1)-(4) in Table 2 but rejected (with a p-value of 0.017 or lower) in favour of a second order polynomial based on an likelihood ratio (LR) test for each model. Similarly, a third order polynomial was considered but the restriction to a second order specification could not be rejected based on an LR test for models (1)-(4) with a lowest pvalue of 0.245. A log-log specification for airport access and distance to nearest train station was considered but rejected with a non-nested model comparison based on the Akaike Information Criterion (AIC).
The omitted noise contour area for each of these models is that closest to the runway, the ANEF 25-30 area. House prices are expected to increase as the intensity of airport noise decreases, implying a positive coefficient for the ANEF 20-25 area, increasing further for the area outside the ANEF 20 contour. Model (1) shows that after controlling for a range of property characteristics houses in the ANEF 20-25 area sell at a premium of around 7% to those in the ANEF 25-30 area, though these differences are imprecisely estimated and significant only at the 10% level. However, houses outside but within 200 meters of the ANEF 20 contour sell for at most 2.4% more than those in the ANEF 25-30 area, a difference that is not statistically significant. Model (3)  A potentially stronger result is expected from models (2) and (4) where the effects of the noise contour areas are allowed to differ before and after the release of the 2008 Essendon airport master plan and the ANEF contour map contained therein. It is expected that the baseline parameters will be smaller and the interaction terms would be larger, both relative to the ANEF indicators in models (1) and (3). In model (2) the price premium in the ANEF 20-25 is found to increase from 5.8 percent to 11.4 percent (5.8+5.6) after the release of the 2008 master plan but these estimates are statistically insignificant. The price premium in the area outside the ANEF 20 contour falls after the release of the 2008 master plan but again these estimates are not statistically significant.
The pattern is similar with model (4)  The parameter estimates on "ANEF 20-25 × After 2008" and "outside 20 × After 2008" in Table 2 correspond to γ and γ in equation (3). The difference in the difference between house prices in the ANEF 25-30 and ANEF 20-25 areas compared before and after the release of the noise contour map in 2008 is given by γ with insignificant estimates of 0.056 for model (2) and 0.172 for model (4), implying the release of the contour map did not change the price difference between these two areas. The equivalent difference in difference between the ANEF 25-30 and the area outside the ANEF 20 contour is given by γ with unexpectedly negative but statistically insignificant estimates.
The results in Table 2 imply that the price of houses south of Essendon airport are not correlated with the ANEF contour areas within which they lie. This result suggests homebuyers are not concerned by airport noise reflected in the 2008 ANEF contour maps.
Even if homebuyers were aware of the contour map information in the years before the release of the master plan, we would expect to see stronger differences for model (1) and (3) in Table 2. The result might be due to homebuyer concerns with aspects of runway proximity other than airport noise, for which the ANEF noise contours may not provide good information. This might include the visual impact of the airport, air quality, traffic, risk of an air crash or other environmental conditions; all factors expected to reduce the price of houses closer to the airport. 15 Conversely, homebuyers may be unaware of noise contour maps when making a decision to purchase a home in the area as there is no regulatory requirement for the master plan or maps to be disclosed to homebuyers. If they are aware, they may struggle to make use of the maps or to identify the location of their potential home on the maps -these maps do not include street names. A bounded rationality argument could be used to justify homebuyer use of a simpler measure of the effects of the nearby runway.
For these alternative explanations, we expect proximity to the airport runway to be negatively related with house price. As a consequence, models from Table 2 (2) and (3) respectively. 16 The parameter estimates for this alternative approach are presented Table 3 with standard errors clustered by street in parentheses. The pattern of results for these specifications is similar to that observed for the noise contour models presented in Table 2, with the R 2 of around 0.703 for all three specifications. However, the critical difference is with the effects of distance to the runway.
Estimates for both the linear and squared terms of straight line distance are significant at the 1 percent level for model (1), while the average marginal effect of distance (in km) is 9.9 percent with a p-value of 0.005. These estimates imply that houses further from the airport runway sell at a premium. Compared to houses closest to the runway (0.247 km), a house with identical observable characteristics but 1.64 km from the runway would sell for 36.8 percent more; this is where the distance effect is at its maximum. The effect of the runway remains positive but decreases to 25.7 percent for the houses at the 90 th percentile of distance from the runway in our estimating sample (2.37 km).
Results are similar for model (2)  Model (3) incorporates interactions between distance from the runway and movements.
Results are statistically significant and consistent with intuition, suggesting the negative effects of proximity to the airport on house price are mitigated by reductions in aircraft movements. Given the interaction terms, we compute average marginal effects. The average marginal effect of annual aircraft movements for houses closest to the runway is computed to be 3.2 percent (p -value = 0.045) while for those 1.64 km from the runway it is 3.9 percent (p -value = 0.018). At higher value of aircraft movements of 70,000 from the earlier years in our sample, the average marginal effect of distance is 14.4 percent (p -value = 0.021) while 16 A log-linear specification of distance to the runway, airport access and distance to nearest train station was considered for models (1) -(3) in Table 3 but rejected (with a p-value of 0.001 or lower) in favour of a second order polynomial based on an LR test for each model. Similarly, a third order polynomial was considered but the restriction to a second order specification could not be rejected based on an LR test for these models with a lowest p-value of 0.702. A log-log specification for distance to the runway, airport access and distance to nearest train station was considered but rejected with a non-nested model comparison based on the Akaike Information Criterion (AIC); for analysis and discussion of flexible specifications of hedonic models see Kuminoff et al. (2010).
at movements of 55,000 from the later years in the sample, the average marginal effect of distance is 13.1 percent (p -value = 0.017).
The estimated quadratic relationship between price and distance to the runway is consistent with the lower, though statistically insignificant, positive coefficients on the outside ANEF 20 indicator variable in Table 2, suggesting the more detailed information in the continuous distance variable relative to the ANEF contour indicators may be aiding the estimation of the relationship.
These results are important because they show that while variation in house prices is not explained by ANEF noise contours, prices do increase for houses located further from the airport runway. The implication is that homebuyers focus on either a simpler measure of the effects of airport proximity, i.e. distance, or they are concerned with more than just noise from the airport which may be better reflected by distance than noise contours.
Focusing on houses between 1.6-1.8 km from the runway, they can be found in both the ANEF 20-25 area and outside the ANEF 20 noise contour. However, it appears the key reason that noise contours do not have a strong relationship with house prices is that many houses outside the ANEF 20 contour are closer than 1.64 km to the runway, with some less than 0.50 km away. Notwithstanding the fact that these houses should not experience excessive airport noise according to the ANEF contour maps, buyers seem to be guided by distance when deciding on the disamenity of the nearby airport and what they should pay for such houses.
We can estimate the economic impact of the discrepancy between distance and noise contours by comparing two properties that are outside ANEF 20 contour and thus unaffected by airport noise but at different distances from the airport. For example, according to model variables are elasticities as we use the logarithms of these variables. 17 The number of bedrooms has a positive and statistically significant impact on house price. Proximity to the freeway does not have a statistically significant negative impact on house value though the sign of the coefficient is consistent with expectations. 18 The relationship between house price and distance to the nearest train station follows a second order polynomial (jointly significant at p = 0.006 based on a F-test) with house prices increasing with distance to the nearest station up to 1.69 km, thereafter decreasing in value with this distance.

Robustness
We undertake a number of checks to test the robustness of these results. The first relates to the impact of neighbourhood dynamics on house prices, we interact time fixed effects with our suburb indicators. For time fixed effects we adopt a more parsimonious series of 16 annual time indicators (1998-2013 inclusive with 1998 as the omitted category). We reestimate models (1) and (2) from Table 2 and model (1) from Table 3 with the results presented as models (1) -(3) respectively in Table 4. The table shows the earlier results are qualitatively unchanged by the allowance for local dynamic effects in house prices.
The effects of the release of noise contour maps may be difficult to identify given the long period over which the models incorporating noise contours are estimated and the potential for amenity values to change over time (Kuminoff et al. 2010). Our first strategy to deal with this is to re-estimate model specifications (1) and (2) from Table 2  values, albeit at the cost of a smaller sample, providing a greater opportunity for the price 17 As a robustness check, we re-estimate the models in Table 2 and Table 3 replacing the log( ) and log( ) variables with second order polynomials of each respective variable; see for example Efthymiou and Antoniou (2013) and Schipper et al. (1998). The results are qualitatively unchanged. 18 It is possible that being located close to the freeway could have a positive impact on house value by improving access to other areas of the city. The travel distance to the closest freeway entrance for each property was calculated and included, as a second order polynomial, in all four models presented in both Table 2 and  Table 3. This distance was not statistically significant, nor did its inclusion qualitatively change the results. Further, as the freeway was constructed between 1996-2000, we re-estimate the models in Table 2 and Table 3 using a sample period of 2001-2013 to avoid any effects of major freeway upgrades completed in 2000. All results are qualitatively unchanged.
effects of any new information in the master plan to be identified in the models. 19 The parameter estimates for the noise contour variables are presented in Table 5. Results on the noise contours are qualitatively unchanged with the difference in difference parameter estimates on "ANEF 20-25 × After 2008" and "outside 20 × After 2008" in model (2) both insignificant, maintaining the result that the release of the contour map did not change the price differences between noise contour areas.
Our second strategy to deal with possible dynamics in the effects of airport noise is to estimate model (1) from Table 2 and Table 3

Conclusion
We have shown that sale prices of homes near Essendon airport in Melbourne, Australia, do not reflect the information in maps of ANEF contours. Instead, house prices are strongly correlated with the straight line distance of the property from the airport runway and annual aircraft movements. The distance relationship is nonlinear with the premium ranging up to 37 percent for houses with identical observed characteristics that are 1.64 km from the runway, while the marginal effect of a 1000 flight reduction (average flights = 62,000) in annual aircraft movements is 4.5 percent. While house prices are uncorrelated with ANEF contours, many of the houses this distance from the runway are located outside the ANEF 20 contour.
However, many houses outside the ANEF 20 contour are closer to the runway than 1.64 km, some within 0.50 km of the runway. According to noise contour maps, these houses are notionally unaffected by noise from Essendon airport and should not sell at the 15 percent discount to those 1.64 km from the runway that they do. We undertook a number of robustness tests with results qualitatively unchanged. Aircraft movements were also found to have an important influence on house prices around Essendon airport but the noise contour and distance effects are robust to the inclusion of aircraft movement.
These results imply that homebuyers seem to favour of straight line distance as a measure of any potential disamenity arising from the nearby airport. This may be because homebuyers focus on distance as a proxy for a wide range of possible externalities from the nearby airport. Given the large literature that identifies the negative relationship between airport noise exposure and house price we are reluctant to interpret the finding as evidence that homebuyers are unconcerned by airport noise. It is more likely that homebuyers are using distance as a rule of thumb to approximate the noise and other effects of the nearby airport.
This latter explanation has important policy implications for the increasing push to better inform consumers. Noise contour maps of areas surrounding airports provide specialised information highly relevant to a range of stakeholders. However, in the market studied here homebuyers may be opting for a simpler more intuitive measure in distance from the runway.
While contour maps meet regulatory requirements, these publicly available maps may not be easily understood by the general public or potential homebuyers may not be aware of their availability or how to access them. Regulators and governments wishing to provide information need to make it easily accessible and simple to understand; see Bts.gov (2017) for an example of such efforts in the U.S. In the case of airports, a possible solution is to require affected home owners or their real estate agents, or both, to provide contour maps to potential buyers of houses near airports. However, improving access to such information is likely to lead to a redistribution of wealth and may be resisted by affected property owners.  Table 2 and Table 3 with quarterly time fixed effects replaced with annual time fixed effects and interacted with noise contours and distance to runway in respective models.    (1) includes property and neighbourhood characteristics along with quarterly time fixed effects. Model (2) adds an interaction between the noise contour variables and an indicator for sales occurring after the release of the 2008 master plan. Models (3) and (4) add annual aircraft movements which is interacted with noise contour variables to models (1) and (2) (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)1998 omitted category) that are interacted with suburb indicators. Models (1) and (2) correspond to models (1) and (2)