A Spatial Analysis of Airport Effects

SERC DISCUSSION PAPER 85
Homeownership and NIMBYism: A
Spatial Analysis of Airport Effects
Gabriel M. Ahlfeldt (SERC, Department of Geography & Environment,
London School of Economics)
Wolfgang Maennig (Department of Economics, University of Hamburg)
July 2011
This work is part of the research programme of the independent UK Spatial
Economics Research Centre funded by the Economic and Social Research Council
(ESRC), Department for Business, Innovation and Skills (BIS), the Department for
Communities and Local Government (CLG), and the Welsh Assembly Government.
The support of the funders is acknowledged. The views expressed are those of the
authors and do not represent the views of the funders.
© G. M. Ahlfeldt and W. Maennig, submitted 2011
Homeownership and NIMBYism:
A Spatial Analysis of Airport Effects
Gabriel M Ahlfeldt*
Wolfgang Maennig**
July 2011
* SERC, Department of Geography & Environment, London School of Economics
** Department of Economics, University of Hamburg
Abstract
This study evaluates the cost of aircraft noise in Berlin, Germany, on the background of the
home-voter hypothesis, which has received increasing attention in the literature. First, we use
exogenous variation in airport noise provided by a series of effective and announced closures
and extensions of airports to identify adjustments in the property market. Second, we
integrate the results of the property market analysis into a spatial analysis of a direct
referendum on an airport closure. Our results indicate that aircraft noise is costly. We observe
significant positive market adjustments to reductions in aircraft noise. Consistently, voters
supported the closure of a city airport where aircraft noise was present and positive price
adjustments from a past announcement had occurred. Homeowners had significantly stronger
preferences than renters, which is in line with the home-voter hypothesis. We conclude
results from direct referenda on public initiatives should be interpreted with care when it
comes to evaluating (expected) environmental effects.
Keywords: noise, rents, referendum, real estate prices, airports, Berlin
JEL Classifications: D61, D62, H41, H71, L83, I18, R41, R58
1 Introduction Strong local opposition to so‐called NIMBY (”Not In My Backyard”) facilities is a political reality in many parts of the world, a fact that complicates the allocation of socially desira‐
ble facilities with negative localized effects (Frey, Oberholzer‐Gee, & Eichenberger, 1996). Airports are a typical example. Although they provide quick access to other cities and re‐
gions for their host communities, they impose a considerable disutility on those living nearby, who are exposed to noise and pollution from air traffic. In this study, we investigate how the stated preferences revealed in a direct referendum of homeowners and renters vary with respect to airport noise, how their votes must be in‐
AHLFELDT / MAENNIG – Are renters better voters? 2 terpreted when evaluating environmental externalities and how distinct incentives to en‐
gage in political bargaining could affect the allocation of NIMBY facilities in the long run. If the home‐voter hypothesis (Fischel, 2001) holds, homeowners should exhibit a stronger opposition to aircraft noise than renters, given that the noise depresses their house values. Renters, in contrast, should be less opposed if they are compensated for the cost of aircraft noise with lower rents. We investigate the case of a direct referendum on closure of the Tempelhof (THF) city airport in Berlin in a spatial precinct‐level analysis. The voting re‐
sults are interpreted in light of the identified real estate price reactions to the effective and announced closure (Tempelhof, THF, and Tegel, TXL) and extension (Schönefeld, SXF) of three airports in Berlin. In contrast to the standard approach used in the literature, our analysis makes use of exogenous variation in noise levels to isolate the treatment effect of aircraft noise from correlated effects, which often cannot be entirely observed. We bring together two strands of research on the evaluation of environmental (dis)amenities in general and airport noise in particular: the assessment via property price capitalization, e.g., Nelson (2004) or Ahlfeldt & Maennig (2010), and voter prefe‐
rences revealed in a direct poll, e.g., on the continued operation of an airfield (Ahlfeldt & Schrayvogel, 2010; Nitsch, 2009). The next section introduces our key ideas with the background of the related literature. Section 3 investigates the property price effects of the (anticipated) change in the pattern of aircraft noise, which are integrated into a spatial analysis of the Tempelhof airport refe‐
rendum in section 4. The final section concludes. In brief, we find evidence for a significant cost of aircraft noise, stronger preferences for homeowners than for renters, which is in line with the home‐voter hypothesis, and, thus, a potential for segregation of public facili‐
ties with negative (positive) local characteristics into areas inhabited by renters (home‐
owners). 2 Background 2.1 Berlin City Airports In the early 1990s, Berlin, partly because of its unique history, possessed three relatively small airports: Tegel (TXL) and Tempelhof (THF), within the boundaries of the former West Berlin, and Schönefeld (SXF), which lies close to the city boundaries to the southeast AHLFELDT / MAENNIG – Are renters better voters? 3 of Berlin and served East Berlin during the division period. This situation called for action for two main reasons. First, it was argued that a new major state‐of‐the‐art airport was required to improve Berlin’s international connectivity and competitiveness and attract new businesses. Second, the two city airports in the former West Berlin were located cen‐
trally and exposed a densely populated area to noise, pollution and crash risk. On July 4, 1996, the so‐called “consensus resolution” (Konsensbeschluss) was established among the mayor of Berlin, Eberhard Diepgen, Brandenburg’s state governor, Manfred Stolpe, and the federal transport minister, Matthias Wissmann. Therein, it was decided to redevelop Schönefeld airport to a large‐scale international hub airport named Berlin‐
Brandenburg International (BBI) where all air traffic would be concentrated so that the Tegel and Tempelhof airports could be closed. Of course, both city airports, because of their central locations, did not only harm many residents living within their flight corridors. They also provided exceptional accessibility for many residents and businesses compared to the more remote location of Schönefeld. As a consequence, businesses and passenger lobbies tried to challenge the decision by taking court action, among other strategies. After years of legal and public disputes, the Berlin Senate released a “funded decision” (fundierter Bescheid) in favor of the shutdown of Tempelhof airport in August 2006. This decision was challenged too, but, after another revision, eventually passed the Federal Administrative Court of Germany (Bundesverwal‐
tungsgericht) as the court of last instance. The closure of Tempelhof airport was scheduled for and became effective on October 31, 2008. As this date approached, the intensity of the protest against the plan steadily in‐
creased. The opposition to the closure of Tempelhof was more strenuous than it was for Tegel, mainly for two reasons. First, the closure of Tempelhof was imminent, whereas Te‐
gel would remain in operation until the inauguration of BBI. Second, Tempelhof was sub‐
ject to strong emotional attachments as a result of its particular history. First, the design of the facilities developed prior to WWII has often been argued to represent a blueprint for modern airports. More importantly, Tempelhof became Berlin’s most important access point for the 1948‐49 airlift established to supply West Berlin residents during the Berlin Blockade. AHLFELDT / MAENNIG – Are renters better voters? 4 The Interest Group of City Airport Tempelhof (ICAT) eventually enforced a referendum in favor of Tempelhof airport remaining in operation. Although the results of the referendum were non‐binding for the Berlin city government, it is widely agreed that a majority vote in favor of continued operation of Tempelhof airport would have de facto exerted strong pressure on the city government to rethink its decision (Nitsch, 2009). The poll was held on April 27, 2008, and won the approval of the majority of voters, but it failed to achieve the minimum quorum of 25% ‘Yes’ votes of the total electorate. More detailed information on the history of Berlin’s airports and the Tempelhof referendum is provided by Ahlfeldt & Maennig (2010) and Nitsch (2009). 2.2 Data The most important variables for the purposes of this analysis capture the exposure to aircraft noise within the affected neighborhoods of the three Berlin airports. From an offi‐
cial report (Laermkartierung nach Umgebungsrichtline, 09.07.2007), exposure to air noise for Tegel Airport is available at a detailed level of 10 x 10‐m grid cells. The noise map for Tegel Airport covers approximately the northern half of the city, including the air corri‐
dors. Within this area, noise levels are recorded for all developed properties and ex‐
pressed in an equivalent long‐term sound pressure index (Lden) in the standard log decibel‐
scale (db). Within a GIS environment, information on aircraft noise is merged with indi‐
vidual property transactions or aggregated at the precinct level by taking representative means over all noise observations in a polling precinct. For Tempelhof, the best available data were obtained from the Berlin airports’ operating company (Flughafengesellschaft) in the form of an electronic map on which iso‐sound pressure zones ranging from 50‐55, 55‐60, 60‐65, 65‐67 and more than 67 dB(A) are de‐
fined. Ahlfeldt & Maennig (2010) employ a regression‐based interpolation to obtain a con‐
tinuous sound surface for a 100 x 100‐m grid for areas that experience sound pressures higher than 45 db. Similar to the Tegel noise pixels, we merged their estimates with ob‐
served property transactions within a GIS environment. Following standard geographic interpolation techniques (Goodchild & Lam, 1980), we generated precinct‐level noise av‐
erages weighted by the shares of precincts’ surface areas that fall into selected noise zones. For Schönefeld airport, which lies outside the boundaries of Berlin and has only a relative‐
ly small part of its air corridor crossing Berlin’s territory, no detailed noise maps were AHLFELDT / MAENNIG – Are renters better voters? 5 included in the noise report. The only available information was a map of the area of re‐
stricted development, which already takes into account the assumption that noise levels will increase considerably when the new international airport, BBI, is inaugurated. We made use of this information in the empirical analyses by defining indicator variables that take a value of one for property transactions and precincts that have centroids that fall into the noise zone and zero otherwise. Figure 1 visualizes the noise surfaces for values beyond 45 db. Note that, throughout the empirical analyses, only aircraft noise exceeding this threshold will be considered. Lower values of aircraft noise are likely to be overlaid by other noise sources and do not capital‐
ize into property prices (Ahlfeldt & Maennig, 2010). We define all noise variables for Tegel and Tempelhof such that they express the number of db exceeding the 45‐db threshold, e.g., values of 5, 10, and 15 would correspond to 50, 55, and 60 db, respectively. Values below 45 db were set to zero to reflect the fact that air noise dissolves in other noise sources. We used an exhaustive record of 74,297 transactions of developed residential properties that took place between January 1, 1990, and December 31, 2009, within the boundaries of the Federal State of Berlin, Germany.1 Throughout our empirical analyses, we distin‐
guish between transactions of a) one‐ or two‐family houses, townhouses and villas (35,161 observations) and b) multi‐family houses (39,163 observations). The transaction data were provided by the Committee of Valuation Experts in Berlin and include the usual parameters, such as age, floor space, plot area, and stories as well as information on land use, physical condition and building type. Employing a GIS environment, property transac‐
tions were geo‐referenced based on geographical coordinates and merged with the framework of the Urban and Environmental Information System of the Senate Department of Berlin (Senatsverwaltung für Stadtentwicklung Berlin, 2006). Within this GIS environ‐
ment, additional environmental control variables capturing the impacts of natural and environmental (dis)amenities, transport and public infrastructures, built heritage as well as noise emissions and airport accessibility variables could be generated. All distances are precise to six significant digits and accurate to the level of addresses when referring to 1 Only relatively few observations had to be excluded from the full record because of missing val‐
ues of crucial characteristics. No signs of a sample selection bias were found. AHLFELDT / MAENNIG – Are renters better voters? 6 transactions. When referring to precincts, distances strictly refer to their geographic cen‐
troids. Data on voting results from the Tempelhof referendum were obtained from the state sta‐
tistical office of Berlin‐Brandenburg for 1201 voting precincts. Of the 881,035 votes that were cast, 650,464 votes from the ballot boxes can be utilized in the empirical analyses. The remaining absentee votes, unfortunately, cannot be considered because of missing geo‐references.2 We used a GIS framework to merge the voting outcome with 2008 data on socio‐demographic characteristics available at the levels of 15,937 statistical blocks (population, age groups, proportion of males and population of non‐German citizens), 191 zip codes (purchasing power) and 2,424 small voting precincts (outcome of the 2006 state elections). These data were obtained from the Statistical Office in Berlin, with the excep‐
tion of data on purchasing power, which were derived originally from a prognosis of the Consumer Research Society (Gesellschaft für Konsumforschung (GfK)). Standard area in‐
terpolation techniques (Arntz & Wilke, 2007; Goodchild & Lam, 1980) were used to aggre‐
gate all data to the level of the 1201 precincts. Although this data set is unusually rich with respect to the set of hedonic property charac‐
teristics, including location features and noise proxy variables as well as precinct neigh‐
borhood attributes, the data set also has one somewhat unfortunate limitation, i.e., we cannot observe the homeownership rate directly at a reasonably fine spatial level. Howev‐
er, because these data are crucial for the purposes of our analysis, we defined a proxy va‐
riable to approximate the rate at the precinct level. We made use of a particularity of the Berlin housing market: a segmentation into detached, semi‐detached and attached single family houses, villas and townhouses, which are almost entirely owner occupied, on one hand (SF), and the typical downtown, usually five‐story apartment buildings, which are almost entirely occupied by renters, on the other. We refer to these separate markets when distinguishing between the homeowners and the renters throughout our empirical analysis. The urban environmental information system of the Senate Department defines 15,937 statistical blocks, based on a range of homogeneity criteria, including the building struc‐
2 Nitsch (2009) shows that when comparing the votes cast at ballot boxes and by mail by district, differences are negligible. AHLFELDT / MAENNIG – Are renters better voters? 7 ture. Based on the assumptions made above, the homeownership rate (HR) for precinct j can be approximated as the proportion of the total population above the age of 18 (P) within the precinct that lives within the boundaries of a statistical block k that is devel‐
oped with single‐family homes (SF). ∑
∑
(1) Note that in unpublished robustness checks, we considered the proportion of transactions that fall into the category of single‐family houses in all transactions within a precinct as an alternative indicator. Although the results are generally consistent, we prefer the defini‐
tion in (1), as it takes into account the much higher number of eligible voters living in an average multi‐family house.3 3 When comparing our approximation for the homeownership rate to the ratio of owned dwellings at total dwelling stock for the 12 city districts, we find a corelation coefficient of close to 0.8. As expected, our indicator yields higher proporitions as a result of accounting for the, in average, larger household size living in detached and attached houses compared to flats. AHLFELDT / MAENNIG – Are renters better voters? 8 Fig. 1 Noise measures Notes: Own illustration based on the Urban Environmental Information System (Senatsverwaltung für Stadtentwicklung Berlin, 2006) 2.3 Basic ideas and related literature Large‐scale (transport) infrastructure projects involve considerable public investment, which is typically justified by correspondingly large external benefits. Besides traditional cost‐benefit analyses, productivity and utility effects are increasingly assessed based on property market analyses. This is possible because, following bid‐rent theory, any increase in accessibility or environmental quality should be offset by an increase in equilibrium rent. A large body of literature has investigated the property price effects of various trans‐
port infrastructure projects, including airports, which are unique in that they induce not only positive economic stimuli for their cities and regions, but also a disamenity related to air traffic at the local level. Nelson (2004) offers a good synthesis of this strand of research by providing a meta‐analysis of 20 studies that investigate noise effects on property prices AHLFELDT / MAENNIG – Are renters better voters? 9 for North American airports.4 As a whole, the evidence clearly points to a depreciating effect of aircraft noise of, on average, about 0.6% for any 1‐db increase. Closely related to the present study, Ahlfeldt & Maennig (2010) investigate the property price effects of the three Berlin airports in a cross‐sectional design. Besides confirming the presence of signif‐
icant costs related to aircraft noise, their results indicate a discontinuity according to which noise has a disproportional effect and an interaction effect that implies that the marginal price effect of aircraft noise diminishes in the presence of alternative noise sources. The evaluation of external aircraft noise effects via property prices has been criticized for a variety of reasons, the most fundamental of which is the difficulty of effectively control‐
ling for all structural and locational characteristics, including zoning policies and various forms of sorting with respect to the relevant (dis)amenities. This is a special case of the broader problem in social science of separating treatments from correlated individual effects. We address this challenge by investigating market reactions to (announced) changes in aircraft noise, i.e., the closure and extension of existing airports, which allows many of the otherwise unobservable features to be held constant and thereby permits better identification of the treatment effect. Similar approaches have recently been em‐
ployed for the evaluation of property price effects of intra‐city (Ahlfeldt & Wendland, 2009; Gibbons & Machin, 2005; McMillen & McDonald, 2004) and, more recently, inter‐city rail lines (Ahlfeldt, in press), but have not yet been applied to airports and the related noise. Nitsch (2009) argues that, to a large degree, the issue of unobserved correlated effects as well as a number of problems with alternative approaches (e.g., happiness surveys, con‐
tingent valuation) can be overcome by investigating voting patterns in direct public polls on specific public projects. Closely related to the present study, Nitsch (2009) and Ahlfeldt & Schrayvogel (2010) investigate the 2008 referendum on the closure of the Tempelhof airfield. Their somewhat distinct conclusions regarding the net effects of aircraft noise, 4 Other interesting surveys include Nelson (1980), Van Praag & Baarsma (2005) and Schipper, Nijkamp, & Rietveld (2002). There are, of course, a large number of other interesting studies on aircraft noise, which are mostly case studies (e.g. Brandt & Maennig, in press; Collins & Evans, 1994; Mieszkowski & Saper, 1978; Nelson, 1979; Pennington, Topham, & Ward, 1990; Tomkins, Topham, Twomey, & Ward, 1998; Uyeno, Hamilton, & Biggs, 1993; Voith, 1991). AHLFELDT / MAENNIG – Are renters better voters? 10 which are clearly negative in Ahlfeldt & Schrayvogel (2010) but less clear in Nitsch (2009), can to some extent be explained by the different proxy variables employed to capture air‐
port externalities; i.e., while Ahlfeldt & Schrayvogel (2010) use aircraft noise, Nitsch (2009) employs approximations via total noise of all sources and/or distance to airfields or noise protection zones.5 In this article, we bring together these two strands of research by providing the first prop‐
erty market analysis of aircraft noise based on exogenous variation and integrating the results into a spatial analysis of the voting pattern in a direct referendum on an airport closure. This integrated approach facilitates a cross‐check for consistency of the two alter‐
native approaches towards the evaluation of environmental (dis)amenities and, as will be discussed, leads to a better comprehension of the voting incentives of different population groups. Nitsch (2009) frames his empirical analysis of the precinct‐level voting outcome in the Tempelhof referendum by three competing hypotheses; if airport noise is costly, prefe‐
rences for the closure of the airfield will be particularly strong within areas that are ex‐
posed to air noise (#1); voters will be indifferent if they are compensated for the cost of noise, e.g., by lower rents (#2); or support in these areas will be particularly strong if vot‐
ers exhibit adaptive preferences (#3). Although these competing hypotheses are intuitive‐
ly plausible, we argue that to fully understand the incentives of voters to engage in the referendum, a distinction between homeowners and renters has to be made. We will de‐
velop our more specific set of hypotheses in section 3. It is important to note that, at this point, our integrated analysis of aircraft noise effects revealed in property prices and voting behavior addresses the ongoing scholarly debate on the home‐voter hypothesis established by Fischel (2001). Accordingly, homeowners will vote in favor of any kind of initiative that they expect will raise the value of their real estate properties. This hypothesis has received support in the empirical literature, e.g., from Bruner & Sonstelie (2003), Brunner et al. (2001), Hilber & Mayer (2009), and, most closely related to our study in terms of the employed methodology, Dehring et al. (2008), who show that the spatial voting pattern in a public referendum on a sports stadium could 5 A more explicit discussion will be provided in section 3. AHLFELDT / MAENNIG – Are renters better voters? 11 be partially explained by real estate price reactions to a series of preceding announce‐
ments. Ahlfeldt (2010a), exploring the voting behavior in a public referendum on a large‐
scale urban development project within a rental environment, shows that renters, in con‐
trast to homeowners, may oppose public initiatives if they are associated with increases in the local area’s valuation. The rationale is that a demand‐driven increase in the cost of living space, despite the accompanying increase in neighborhood quality, will drive resi‐
dents who remain in the area out of their consumption optima. They would be forced to consume too much – if not the wrong – neighborhood quality at the expense of consump‐
tion of non‐housing goods. For the subject referendum, these findings imply that, given that real estate markets discount properties on aircraft noise, homeowners, for a given level of (perceived) noise‐related costs, would strictly have larger incentives to vote against the continued operation of an airfield than renters would. These findings have important implications for the efficiency of the allocation of public goods with local characteristics, be they local benefits or costs. Airports, in some sense, fall into the category of so‐called NIMBY (“Not In My Backyard”) facilities, a mixture of public good at the metropolitan level (market access to other regions and countries) and bad at the local level (costs related to noise and pollution). As discussed in recent research, local opposition to NIMBY facilities makes the provision of such socially desirable facilities in‐
creasingly complicated (Feinerman, Finkelshtain, & Kan, 2004; Frey et al., 1996). If there are asymmetric incentives for homeowners and renters to engage in political bargaining, an allocation of these facilities via direct democracy need not be optimal in terms of either efficiency or equity.6 Also, even though a direct vote on a public facility with local charac‐
teristics, e.g., an airport, might provide the “ultimate feedback on an eligitable voters overall assessment of the costs and benefits” (Nitsch, 2009), it is not necessarily a direct vote on the environmental (dis)amenities such facilities emanate. If a related initiative is expected to have a positive (negative) impact on local property markets, homeowners will have an incentive to support (oppose) the initiative, whereas renters, as discussed, will be reluc‐
tant to cast positive (negative) votes. Only in the absence of expected effects on prices and rents would a direct vote on the operation of an airport reveal unbiased preferences on 6 Although NIMBY facilities in the long run would end up being concentrated in rental environ‐
ments, facilities with positive localized effects would be concentrated in areas inhabited by homeowners. A concentration of environmental disamenities could in turn lead to social segre‐
gation and a downward spiral in disadvantaged areas, involving considerable social costs. AHLFELDT / MAENNIG – Are renters better voters? 12 the related environmental effects. In reality, even with rent regulations in almost every developed country of the world, this scenario is unlikely to apply to renters, and certainly not true for homeowners. In any case, the anticipated effects on houses and rents as well as the local composition of renters and homeowners must be taken into account to fully understand and interpret voters’ attitudes expressed in a direct public referendum. We will turn our attention to this issue in the next section. 3 Property price effects 3.1 Strategy This section represents the first stage of our empirical analysis, where we identify the price adjustments to announced and effective changes in air noise patterns in Berlin. As discussed, the relevant literature is dominated by cross‐sectional analyses that attribute (conditional) price differentials between properties to the presence of aircraft noise. In contrast, our research strategy estimates the treatment effect from (anticipated) varia‐
tions in aircraft noise, i.e., the closure or extension of airfields. Similar to Ahlfeldt (2010a), we argue that price effects for renter‐occupied multi‐family houses reflect actual or ex‐
pected changes in rents as the transaction price corresponds to the discounted stream of rental revenues.7 Our identification strategy is based on a combined hedonic and quasi‐experimental differ‐
ence‐in‐difference (DD) framework. We follow a long tradition in the hedonic property literature that goes back to Rosen (1974) and correct property prices for a variety of structural and location characteristics. Furthermore, we exploit the time dimension of our data set to control for unobservable characteristics at the precinct level. Using the noise treatment variables discussed in the data section, we estimate the change in the (margin‐
al) price effect of aircraft noise following selected announcements relative to areas that are not affected by noise. Our approach shares much in common with a recent strand in the literature that uses DD or RDD (regression discontinuity designs) to identify the effects of local policy interventions. Dachis, Duranton & Turner (2009) provide a discussion of the 7 See, e.g., McMillen and McDonald (2004) for a more detailed discussion. AHLFELDT / MAENNIG – Are renters better voters? 13 underlying assumptions of these quasi‐experimental designs. In an extension of the major‐
ity of these studies, we define noise treatments for Tegel and Tempelhof based on a conti‐
nuous rather than a discrete variable to distinguish between the degrees to which individ‐
ual properties are subject to treatment. As in all quasi‐experimental research of this kind, the careful definition of a control group is crucial to establishing an appropriate counterfactual. Specifically, it is critical that the treatment and control groups, in the absence of the analyzed intervention(s), would follow the same homogenous trends. This is a rigid assumption for a metropolitan area over an observation period of 20 years and even more so for Berlin, with its unique post‐
unification history. Besides the frequently employed sets of time (year) and location (pre‐
cinct) effects, our empirical specification, therefore, features a full set of (20) years x (23) districts (Bezirke) fixed effects that accommodate shocks that are common to homogenous city areas, here defined as city districts. As shown in Figure 1, all city districts have large proportions of surface area unaffected by aircraft noise and, thus, offer sufficiently large control areas. We note that the dis‐
trict × year cells capture processes of spatial reorganization that are specific to the case of post‐unification Berlin, i.e., a steady relative appreciation of the areas belonging to former East Berlin as well as a re‐emergence of the historic downtown area in “Mitte” and a cor‐
responding price gradient. To specifically accommodate the latter, we also introduce a set of year × distance to the central business district (CBD) effects. Last, we introduce a set of (daily) trend × noise interactive terms to estimate the announcement effect conditional on a linear relative trend to rule out the possibility that the estimated announcement effects represent an artifact of a continuous long‐run adjustment within neighborhoods that is accidently correlated with the noise treatments. We define three periods starting at critical announcement dates: a) the consensus resolu‐
tion (Konsensbeschluss) to develop a new international airport adjacent to Schönefeld Air‐
port and to close down the airports in Tegel and Tempelhof in July 1996, b) the funded decision on the closure of Tempelhof airport in August 2006, which removed much of the uncertainty regarding the closure of the airport, and c) the failure of the Tempelhof refe‐
rendum, after which the final closure of the airfield could be considered as certain. The three periods are each denoted by indicator variables , which take the value of one for the period following the respective announcement Z = {a,b,c}. Evidently, the announce‐
AHLFELDT / MAENNIG – Are renters better voters? 14 ments b) and c) are specific to Tempelhof airport. However, areas within the air corridor of Schönefeld airport could potentially be affected by these announcements, as air traffic would be shifted to the new major airport. Although the closures of the Tempelhof and Tegel airports are not directly linked, any progress in the closing of Tempelhof would in‐
crease the credibility of the initial consensus decision and, thus, remove uncertainty re‐
garding the eventual closure of Tegel airport. Because all announcements thus could po‐
tentially have an influence on property prices in all treatment zones, our empirical specifi‐
cation includes a full set of noise (NF) × announcement (
) effects for all airports, F={THF,TXL,SXF}, and all announcements Z={a,b,c), where the estimated treatment effects are revealed by the set of coefficients log
∑ ∑
∑ ∑ Ω
∑
∑
. The full specification takes the following form: ∑
∑
∑
∑
∑
(2) where Pit is the price per square meter of land area of a property i realized in a transaction at time t, and Sk are the structural and Ll the locational characteristics of the properties. Ahlfedt (2010b) provides a detailed hedonic analysis of the Berlin housing market, includ‐
ing a motivation for the selected controls. are a full set of precinct fixed effects that con‐
trol for unobserved time‐invariant characteristics. Year (
) effects are interacted with district dummy variables (Bb) and distance to the CBD, TR is a daily trend variable and stand for a set of indicator variables, each denoting a month m from February to Decem‐
ber. Standard errors are clustered on precincts so that specification (2) facilitates mean and variance shifting across space and controls for within‐precinct spatial auto‐
correlation. After identifying a critical announcement date, the spatial market response can be ex‐
plored by fitting a regression surface to the conditional average price differentials before and after the intervention, which can then be mapped. In a related approach, Dehring et al. (2008) attribute the market response to an announcement of plans for a sports stadium based on the plain distance of a transaction to the location of the stadium. Given that a concentric approximation would fall short in accounting for the noise pattern that emerges along the air corridors, our surface requires a larger degree of functional flexibili‐
ty. Therefore, we define three coordinate systems with x‐axes that follow the runway paths of an airport F, with y‐axes standing upright on the center of the airfield. We define a AHLFELDT / MAENNIG – Are renters better voters? 15 surface as a linear combination of third‐order polynomials of XF and YF values and respec‐
tive XF × YF interactive terms. ∑ ∑
|
|
|
|
|
| (3) The announcement surface is then estimated based on equation (2), where all noise treatment variables are substituted by surface variables from (3) as well as an interactive term with an announcement indicator variable, . log
∑
∑ ∑
∑
where the set of estimated coefficients, ∑
∑
∑
(4) , in conjunction with the coordinate variables forms the announcement surface. To focus on the discontinuity induced by the interven‐
tion, we restrict the sample to observations that fall within one year prior to and after the considered announcement. The estimated surface serves as a cross‐check for whether market perceptions follow the current noise pattern. Moreover, it can be used in the second stage of the analyses to eva‐
luate whether subjective noise perception expressed in the referendum matches the mar‐
ket reaction capitalizing into property prices and to test the home‐voter hypothesis (Dehr‐
ing et al., 2008). If the hypothesis holds, positive price signals should be negatively asso‐
ciated with support for the Tempelhof airport. Clearly, if noise effects are at all present, we expect positive reactions to the announcement of the construction of a new major airport within the impact areas of Tegel and Tempelhof and a negative reaction along the Schöne‐
feld air corridor. 3.2 Results Table 1 shows the estimated announcement effects corresponding to equation (2) for the sample of transactions of single‐family properties. Besides the full and preferred specifica‐
tion in column (6), estimates are presented for a range of reduced specifications in col‐
umns (1) to (5). Estimated effects of hedonic characteristics are presented in Table A1, column 1, for the full specification. They are generally in line with conventional expecta‐
tions, though some location variables are insignificant. AHLFELDT / MAENNIG – Are renters better voters? 16 There are few significant effects for single‐family houses in the precinct of Tegel airport. Potentially, the foreseen closure was too far in the future or remained too uncertain to be capitalized at the considered announcement dates. Similarly, no significant adjustments are evident for the Schönefeld area, at least once precinct fixed effects are introduced. In contrast, we find robust positive adjustments following the announcements of the closure of Tempelhof airport for single‐family houses in that area. Only in model (1), where homo‐
genous city‐wide trends are assumed, could no significant effects be established. This re‐
sult highlights the importance of relaxing this clearly unrealistic assumption. In all other models, there is a positive and significant market reaction to the plans to remove the noise disamenity along the Tempelhof air corridor. Once precinct effects are introduced, another significant reaction following the failure of the Tempelhof referendum becomes evident. Interestingly, the market adjustments were of similar size when the closure was initially announced (a) and finally became certain (c). In each case, there was an adjustment of roughly 2 percent per 1 db of noise level. For a mean noise level within the Tempelhof airport noise area of about 5 db (in excess of the 45 db threshold), the accumulated effect amounts to roughly 20%. AHLFELDT / MAENNIG – Are renters better voters? 17 Tab. 1 Locational price effects of three airport decisions: Single‐family houses Area x Decision (1) (2) (3) (4) (5) (6) Tempelhof x A) 0.002 0.011** 0.017** 0.021** 0.019** 0.021** (0.004) (0.004) (0.004) (0.007) (0.004) (0.007) Tempelhof x B) 0.007 0.010+ 0.008 0.01 0.002 0.004 (0.006) (0.006) (0.006) (0.007) (0.006) (0.007) Tempelhof x C) 0.002 0.004 0.004 0.005 0.018* 0.019* (0.007) (0.008) (0.008) (0.008) (0.008) (0.008) Tegel x A) ‐0.001 0.001 0.003* 0.001 0.001 0.002 (0.001) (0.001) (0.001) (0.002) (0.001) (0.002) Tegel x B) 0 0 ‐0.001 ‐0.002 ‐0.002 ‐0.002 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Tegel x C) 0.002 0.002 0.002 0.002 0.003 0.003 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Schönefeld x A) 0.177** 0.027 ‐0.011 0.129* ‐0.009 0.052 (0.035) (0.039) (0.039) (0.065) (0.039) (0.064) Schönefeld x B) ‐0.102* ‐0.106* ‐0.089* ‐0.007 ‐0.018 0.016 (0.041) (0.043) (0.044) (0.054) (0.043) (0.052) Schönefeld x C) ‐0.028 ‐0.005 ‐0.007 0.025 ‐0.056 ‐0.042 (0.05) (0.053) (0.054) (0.055) (0.052) (0.053) Struct. Controls YES YES YES YES YES YES Loc. Controls YES YES YES YES YES YES Precinct Effects ‐ ‐ ‐ ‐ YES YES Year Effects YES ‐ ‐ ‐ ‐ ‐ Month Effects YES YES YES YES YES YES Dairy Trend ‐ ‐ ‐ YES ‐ YES Year x District ‐ YES YES YES YES YES Effects Year x CBD ‐ ‐ YES YES YES YES Effects Noise x Trend ‐ ‐ ‐ YES ‐ YES Effects Sample Single‐fam Single‐fam Single‐fam Single‐fam Single‐fam Single‐fam
Observations 35,098 35,098 35,098 35,098 35,098 35,098 R‐squared 0.76 0.78 0.78 0.78 0.8 0.8 Notes: Dependent variable is the log of property prices per square meter of land area in all mod‐
els. Tempelhof and Tegel area measures of aircraft noise in db exceeding 45 db. A), B) and C) are indicator variables denoting the period after a) the consensus resolution (Konsens‐
beschluss) to develop a new international airport adjacent to Schönefeld Airport and to close down the airports in Tegel and Tempelhof in July 1996, b) the funded decision on the closure of Tempelhof airport in August 2006 and c) the failure of the Tempelhof ref‐
erendum. Standard errors (in parentheses) are clustered on precincts. +/*/** denote sta‐
tistical significance at the 10/5/1 % levels. Table 2 repeats Table 1’s models for the sample of multi‐family houses. Again, we restrict the presentation to announcement effects to save space and show hedonic estimates for the full specification (6) in Table A1 (column 2) in the appendix. We omit announcements in the Schönefeld airport area when looking at multi‐family houses, as the area is domi‐
AHLFELDT / MAENNIG – Are renters better voters? 18 nated by single‐family properties and there are not enough observations within the noise protection zone to establish a reliable counterfactual.8 Estimates for the hedonic controls, again, are largely in line with expectations.9 For the considered announcements, we find the somewhat surprising result of no significant posi‐
tive adjustment for the Tempelhof or for the Tegel areas. For announcement b), the court decision in which all appeals by air carriers against the scheduled closure of Tegel were rejected, we find a significant negative market reaction in the Tegel air corridor. This find‐
ing could indicate that at least some agents on the market valued proximity to a major city airport, most likely because of accessibility effects. Minimally, (perceived) positive exter‐
nalities outweighed negatives for this submarket. Besides the possibility that renters in the tenant blocks simply experienced a lower disutil‐
ity from aircraft noise around Tempelhof airport than owners of single‐family houses be‐
cause of unobserved characteristics and preferences, there are two major explanations for the absence of significant announcement effects.10 First, Ahlfeldt & Maennig’s (2010) find‐
ings indicate that within the dense downtown areas of multi‐family tenant blocks, other noise sources seem to dominate the effect of aircraft noise. A reduction in the level of air‐
craft noise will consequently have smaller effects if alternative noise sources, in particular road noise, remain unchanged. Second, adjustments in property prices of renter‐occupied multi‐family houses may simply be smaller because of rent regulations, which slow down the adjustment of expected rental revenues. Although, in the German housing market, rents can be adjusted freely as soon as tenants vacate an apartment and a new contract is signed, all residents who stay put are protected against excessive rent increases. In any case, the absence of clear and positive market reactions to the investigated an‐
nouncements/decisions is an important finding for the second stage of the analysis, as 8 Only 81 transactions of multi‐family properties took place in this area over a 20‐year period (as opposed to 4,484 for the Tempelhof noise area and 7,264 for Tegel). 9 Some location variables, e.g., distance to water or distance to main streets and road noise, in this case most probably a collinearity problem, show unexpected changes. These findings, however, are less concerning when recalling that the variables only make use of the within‐precinct varia‐
tion. 10 A potential explanation for the relatively high impact of aircraft noise on house prices paid by homeowners may be a higher risk premium for the uncertain disutility from aircraft noise. Ren‐
ters, who are more mobile, would naturally have a lower risk premium. AHLFELDT / MAENNIG – Are renters better voters? 19 rising prices would be (noisy) signals of expected rent increases. Residents who are pro‐
tected by regulation and need not heed significant signals of potential rent increases re‐
lated to the closure of the city airport(s) have little incentive to base their voting decision on other considerations besides the pure (dis)utility effect derived from the operation of the airport. Tab. 2 Locational price effects of three airport decisions:: Multi‐family houses Area x Decision (1) (2) (3) (4) (5) (6) Tempelhof x A) ‐0.007* 0.004 0.005 0.004 0.003 ‐0.002 (0.003) (0.003) (0.003) (0.007) (0.003) (0.007) Tempelhof x B) ‐0.004 ‐0.005 ‐0.005 ‐0.006 ‐0.004 ‐0.006 (0.003) (0.003) (0.003) (0.004) (0.003) (0.004) Tempelhof x C) 0 0.001 0.001 0.001 ‐0.002 ‐0.003 (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Tegel x A) ‐0.003+ 0 0.001 ‐0.001 ‐0.003 ‐0.002 (0.002) (0.002) (0.002) (0.003) (0.002) (0.003) Tegel x B) ‐0.015** ‐0.005+ ‐0.006* ‐0.007* ‐0.006* ‐0.005* (0.002) (0.002) (0.002) (0.003) (0.002) (0.003) Tegel x C) ‐0.007** 0.002 0 ‐0.001 0.001 0.002 (0.003) (0.003) (0.003) (0.004) (0.003) (0.004) Struct. Controls YES YES YES YES YES YES Loc. Controls YES YES YES YES YES YES Precinct Effects ‐ ‐ ‐ ‐ YES YES Year Effects YES ‐ ‐ ‐ ‐ ‐ Month Effects YES YES YES YES YES YES Dairy Trend ‐ ‐ ‐ YES ‐ YES Year x District ‐ YES YES YES YES YES Effects Year x CBD ‐ ‐ YES YES YES YES Effects Noise x Trend ‐ ‐ ‐ YES ‐ YES Effects Sample Multi‐fam Multi‐fam Multi‐fam Multi‐fam Multi‐fam Multi‐fam Observations 38,991 38,991 38,991 38,991 38,991 38,991 R‐squared 0.66 0.69 0.69 0.69 0.72 0.72 Notes: Dependent variable is the log of property prices per square meter of land area in all mod‐
els. Tempelhof and Tegel area measures of aircraft noise in db exceeding 45 db. A), B) and C) are indicator variables denoting the period after a) the consensus resolution (Konsens‐
beschluss) to develop a new international airport adjacent to Schönefeld Airport and to close down the airports in Tegel and Tempelhof in July 1996, b) the funded decision on the closure of Tempelhof airport in August 2006 and c) the failure of the Tempelhof ref‐
erendum. Standard errors (in parentheses) are clustered on precincts. +/*/** denote sta‐
tistical significance at the 10/5/1 % level. Evidence suggests that the initial announcement (a) of the reorganization of Berlin’s air traffic led to a positive adjustment in the market price of single‐family properties, at least within the air corridor of Tempelhof airport. We investigate this intervention in more de‐
AHLFELDT / MAENNIG – Are renters better voters? 20 tail by estimating a spatial surface of price reactions to the announcement according to equation (4). Baseline hedonic results are listed in Table A1, column 3, in the appendix. A Wald test rejects the null hypothesis of no joint impact of all surface variables at the 1% level. Figure 2 shows the estimated market reaction to the auxiliary coordinate systems defined for the three airports. Specifically, we map the predicted changes in average prop‐
erty prices (in log‐differences) for the 1,201 voting precincts based on the estimated coef‐
ficients, , and the locations of the precincts’ centroids within the airfield coordinate systems. The spatial pattern in Figure 2 resembles the noise map (Figure 1). In particular, there was a positive market reaction to the initial announcement (a) of the air traffic reorganization along the air corridor of Tempelhof airport (the x‐axis of the respective coordinate sys‐
tem). Similar to the noise surface, price effects do not spread concentrically but mainly along the air corridor and much less to the north and south (y‐axis). For the same an‐
nouncement, a similar but somewhat less precise pattern is revealed for Tegel airport. For both city airports, the initial announcement of their closure thus led to a relative apprecia‐
tion in the valuation of single‐family houses. For Schönefeld airport, the picture is also supportive of the presence of air noise‐related costs. The announcement of the redeve‐
lopment of the airfield into a major international hub airport led to an adverse effect on property prices along the air corridor. This is in line with noise‐related costs and an antic‐
ipated increase in noise as traffic is shifted from Tegel and Tempelhof to the new major airport. The only exceptions are a few precincts immediately adjacent to the city boundary and the existing runway. Possibly, at these extremely close areas, the disutility from air noise exceeds a critical value anyway, so further increases would have only modest effects. The positive effect of having a state‐of‐the‐art international airport close by could there‐
fore dominate the disamenity effect. A similar rationale potentially applies to the south‐
western locations close to the new airport but outside of the air corridor. AHLFELDT / MAENNIG – Are renters better voters? 21 Fig. 2 Estimated announcement surface Notes: Own calculation and illustration. Classes are defined based on the Jenks (Jenks, 1977) algorithm. 4 Spatial voting pattern 4.1 Strategy In the second stage of our analysis, we provide a spatial analysis of the referendum on the closure of the Tempelhof airport. We thereby integrate the results from the section above and pay special attention to the differences in the voting behavior of renters and home‐
owners. Renters, as a result of rent regulations and the absence of measurable appreciation of mul‐
ti‐family houses, which would have been indicative of (expected) rent increases, should have an incentive to reveal their true preferences on the environmental (dis)amenity ef‐
fects. In the presence of significant (perceived) disutilities from aircraft noise, we expect the support for the continued operation of Tempelhof airport to be significantly reduced within precincts that are exposed to significant air noise from the airport (Hypothesis I). As discussed, there is at least an indirect connection to the neighborhood of Tegel airport. AHLFELDT / MAENNIG – Are renters better voters? 22 If the decision to close Tempelhof airport had been revised, the whole consensus resolu‐
tion would have been questioned, including the scheduled closure of Tegel airport. Hence, we expect below‐average support for the referendum in areas that are exposed to air noise from Tegel, although the treatment effect should be somewhat attenuated (Hypothe‐
sis II). Finally, given that the closure of Tempelhof (and Tegel) airport would shift all air traffic to Schönefeld, support for Tempelhof’s continued operation should be relatively higher within areas that fall within the noise protection zone of the new major airport, BBI (Hypothesis III). Homeowners, in the presence of positive price signals, have an additional incentive to en‐
gage in the referendum, as they would benefit from an increase in the value of their prop‐
erties. In other words, taking the results from the previous section as given we expect homeowners to cast their votes to increase the value of their properties. Positive price signals from past announcements as well as the proportion of homeowners within a pre‐
cinct should therefore be negatively correlated with the support of the referendum (Hypo‐
theses IV and V). To test these hypotheses, we follow the logic of applied public choice and explain the vot‐
ing outcome in a set of precincts j by a set of socio‐economic attributes (Lo) of the resident population. To accommodate the fact that residents within the former East Berlin may feel less attached to a West Berlin airport, the vector of variables Lo includes an indicator vari‐
able that takes the value of one for precincts within the boundaries of the former East Ber‐
lin. The proportion of votes for major conservative opposing parties in the 2006 federal elections is included to capture loyalty and strategic effects as well as attachments and lifestyle preferences of supporters of the Christian Democratic Party (CDU) and Liberals (FDP), who strongly supported the referendum. The proportion of votes for the Green par‐
ty (Die Grünen) is included to accommodate this group’s specific sensitivity to environ‐
mental issues. Proportions of age groups are included as households at different life stages potentially receive a distinct disutility from aircraft noise. Purchasing power per capita is included to accommodate environmental preferences that are correlated with income. Given that only EU citizens were entitled to vote in the referendum, the proportion of the non‐German population serves mainly as a neighborhood variable for unobserved charac‐
teristics that are correlated with the proportion of the foreign population. AHLFELDT / MAENNIG – Are renters better voters? 23 Our noise measures (NF) for the three airports are included to test hypotheses I‐III. Simi‐
larly, the estimated announcement surface as well our proxy for the homeowner‐
ship rate (HR) are introduced to test hypotheses IV and V. Naturally, the marginal effects of the price signals as well as the proportion of homeowners may have varying effects within the different noise zones (and the rest of the area). We accommodate these effects by in‐
troducing interactive terms of the treatment variables with a set of indicator variables, which equal one for precincts within the respective noise zone and zero otherwise (DNF). We expect the coefficients of these interaction terms to be negative for Tegel and Tempel‐
hof and positive for Schönefeld. ∑
∑
∑
∑
(5) where PctYES is the share of yes‐votes at the turnout in our baseline specification. Robust‐
ness tests are conducted using as alternative dependent variables turnout and the shares of yes‐votes and no‐votes by eligible voters to accommodate heterogeneous incentives to engage in the referendum. All variables that express proportions are expressed in percen‐
tages (multiplied by 100). Because our estimated announcement surface is a generated regressor, we bootstrap standard errors in 500 replications when including the variables in the model to avoid potential bias in OLS standard errors (Murphy & Topel, 2002). Fur‐
ther robustness tests include an extension of specification (5) to control for accessibility effects of Tempelhof airport, a weighted regression that takes into account heterogeneity in precinct size and reduced models that omit socio‐demographic features that are poten‐
tially endogenous to the treatment and, thus, “bad controls” as defined by Angrist & Pischke (2009). In an alternative approach to evaluating the heterogeneity in the effect of the noise treat‐
ment with respect to the (approximated) homeownership rate, we estimate a reduced form of specification (5) for a range of subsamples s, where each sample encompasses a set of precincts within a different range of homeownership rates: ∑
∑
(6) Precisely, we create 81 samples, each one covering a band of 20 percentage points, start‐
ing with 0‐20, 1‐21, etc., and ranging up to 80‐100. The estimated treatment effects ̂ can then be compared across various levels of homeownership. This approach allows the mar‐
AHLFELDT / MAENNIG – Are renters better voters? 24 ginal effects of all socio‐economic controls Lo to vary across different levels of homeow‐
nership. Given the incentives discussed above, we expect the magnitude of marginal noise effects to increase with the homeownership rate. 4.2 Results Figure 3 maps the main response variable analyzed in this section, the share of yes‐votes in the referendum on the continued operation of Tempelhof airport. From the map, a strik‐
ing east‐west heterogeneity is evident. The (unconditional) mean approval rate for the referendum is approximately twice as high within the former West Berlin as it is in the former East Berlin (70% vs. 35%). Similarly impressive, there is clearly increased support for the continued operation of Tempelhof airport within the Schönefeld noise protection zone, whereas similar effects around Tegel and Tempelhof are less obvious. Fig. 3 Share of yes‐votes in the referendum on the continuation of Tempelhof airport Notes: Own illustration based on the Urban and Environmental Information System of the Se‐
nate of Berlin (Senatsverwaltung für Stadtentwicklung Berlin, 2006). Classes are defined based on the Jenks (Jenks, 1977) algorithm. AHLFELDT / MAENNIG – Are renters better voters? 25 Empirical results for equation (5) are presented in Table 3. Column (1) starts with a re‐
duced model specification featuring only non‐noise‐related variables. We add noise indica‐
tors in model (2), which serves as a baseline specification in the remainder of the analysis. The next columns add a set of 10 mutually exclusive 1‐km distance rings based on road distances from precinct centroids to the location of the Tempelhof terminal building (3), announcement effects (4), homeowner variables (5) and, last, announcement and home‐
owner variables jointly (6). All baseline control variables exhibit a high degree of stability. The difference in average approval between the former West Berlin and the former East Berlin is only slightly re‐
duced in our conditional estimates and is still close to 30 percentage points and highly statistically significant. Thus, as expected, history clearly matters. Our political variables also exhibit the expected signs, with increased support in precincts with a high proportion of supporters of conservative parties and reduced support in precincts with more suppor‐
ters of the Greens. Age variables reveal that, relative to the base group of voters aged 27 to 45 years, in areas with a high proportion of families or young voters, support was reduced, potentially as a result of a higher awareness of environmental issues among the younger population. Similarly, relative to the base group of voters aged 27 to 45 years, opposition increases in precincts with higher proportions of the population beyond the age of 45, potentially because of higher noise sensitivity. Of the baseline controls, only purchasing power and non‐German do not exhibit a robust and significant impact, indicating that hete‐
rogeneity in noise perceptions with respect to income is of more limited relevance. Most importantly, all noise treatments significantly impact the voting outcome in our baseline specification at the 1% level (2). A 1‐db increase in aircraft noise triggers a reduc‐
tion in the share of yes‐votes by close to 1 percentage point for Tempelhof and about 0.2 percentage points for Tegel. Given the maxima of the observed noise level at the pre‐
cinct average of about 14 db at Tempelhof and 30 db at Tegel (again in excess of the 45 db threshold), these estimates correspond to a reduction in the share of yes‐votes by up to 14 (Tempelhof) and 6 (Tegel) percentage points. As expected, the effect for Tegel air noise is smaller than for Tempelhof because of a less direct connection to the referendum. In line with Figure 3, the approval rate within the noise zone at Schönefeld is significantly in‐
creased by as much as 32 percentage points. Overall, these findings provide support for hypotheses I‐III. AHLFELDT / MAENNIG – Are renters better voters? 26 These results vary from Nitsch’s (2009) finding of significantly increased support for the continued operation of Tempelhof in proximity to the airport. Based on this finding, Nitsch concludes that adaptive preference is present, with a bias towards the status quo (his hy‐
pothesis 3). These differences in the results must be understood against the background of the employed empirical models, which differ in at least two respects. First, Nitsch consid‐
ers controls for east‐west heterogeneity and political party affiliation, but not for the other socio‐economic attributes considered in our specification. We note, however, that the ro‐
bustness check where those variables are omitted yields almost the same treatment ef‐
fects as in our baseline specification (see Table A2, column 2). Second, we follow Ahlfeldt & Schrayvogel (2010) and estimate the treatment effect based on the average Tempelhof (and Tegel) aircraft noise level within a given precinct, whereas Nitsch builds his argu‐
ment mainly on proximity effects revealed by plain distance measures.11 Although positive proximity effects could result from adaptive preferences as argued by Nitsch, taking his and our results together, it seems likely that his results are driven by broader airport ac‐
cessibility and proximity effects rather than specifically by exposure to aircraft noise. To avoid a downward bias in the magnitude of the estimated noise treatment, which could result from positive accessibility effects and a correlation between access to the airport and aircraft noise, we extend our baseline specification by a set of dummy variables that denote 10 mutually exclusive 1‐km distance rings defined based on effective road dis‐
tances to the Tempelhof airport terminal. Estimated noise treatments, however, remain virtually unchanged, alleviating these concerns (Table 2, column 3). In further robustness checks, presented in Table A2, we replace the dependent variable with turnout (2), share of yes‐votes (3) and share of no‐votes among eligible voters (4), respectively. The results basically confirm the findings from the baseline specification. Within the area exposed to Tempelhof air traffic noise, there was a significantly increased turnout driven by an increase in no‐votes. In contrast, no significant increase in yes‐votes could be found, which implies that within the noise zone, opponents rather than propo‐
nents were mobilized. Based on these findings, we cannot support Nitsch’s hypothesis 3 that adaptive preferences dominated the votes of residents living within the Tempelhof air 11 He also considers average noise levels across all noise sources, but this variable, too, is not spe‐
cific to aircraft noise. AHLFELDT / MAENNIG – Are renters better voters? 27 corridor. Still, the – in terms of magnitude – considerably lower treatment effect found for Tempelhof (and Tegel) airports compared to Schönefeld airport could be interpreted as evidence for adaptive preferences. Voters who became accustomed to the status quo in‐
curred a lower disutility from an environmental externality compared to those who were not exposed to it. Two further robustness checks were conducted to check for potential spatial specification problems. First, we ran a weighted regression where observations with larger numbers of eligible voters received proportionally higher weights to prevent our results from being driven by marginal precincts. Second, LM‐tests detected a significant degree of spatial au‐
to‐correlation, pointing to the appropriateness of a spatial error‐correction model.12 Both specifications support our baseline specification and yield qualitatively similar results (Table A2, columns 5 and 6). In a last robustness check we replace the continuous treat‐
ment variables for Tegel and Tempelhof aircraft noise with indicator variables indicating area that are exposed to more than 45 db noise pressure across the precinct average. In line with the presented evidence we find a robust negative effect on the share of yes‐votes of about 8‐8.5 percentage point within the Tempelhof noise and a reduction of about 2.7 percentage point for the Tegel air zone once socio‐demographic factors are accounted for. In the reminder of this section, we turn our attention to hypotheses IV and V, which are both derived from the home‐voter hypothesis (Fischel, 2001). Therefore, we replace our noise treatment variables with announcement effects (Table 3, column 4), extend the baseline specification by homeownership variables (column 5) and, finally, estimate the full specification (6) (Table 3, column 6). We find that, within areas that are exposed to aircraft noise, positive price reactions to the initial announcement are associated with decreased (increased) support for the referen‐
dum in the Tegel and Tempelhof (Schönefeld) noise areas (column 4). These findings prin‐
cipally support hypothesis IV. We note, however, that in our case some care is appropriate in interpreting this past announcement as a signal of potential price reactions that causally impacts voting decisions (Dehring et al., 2008). First, if this were the case, we would ex‐
12 The error‐correction model (Anselin, 1995) corrects for the spatial structure as follows: , where W is a row‐standardized contiguous weight. LM test statistics are: LMlag = 515.02, robust LMlag = 111.45, LMerror = 549.17, robust LMerror = 145.61. AHLFELDT / MAENNIG – Are renters better voters? 28 pect the announcement effects derived for owner‐occupied single‐family houses to impact only – or, at least, much more strongly – homeowners’ votes, but not the decisions of ren‐
ters. Robust evidence for such heterogeneity could not be found in a range of unpublished robustness checks. Second, if the impacts of the announcement variables are estimated conditionally following our noise signals, the respective coefficients, though exhibiting the expected signs, do not prove to be statistically significant. We therefore prefer interpreting the results from column (4) more carefully. Even a more careful interpretation, however, has important implications, i.e., a) the estimated market reaction was likely to be driven by aircraft noise and b) both approaches to the evaluation of the environmental disamenity yield (spatially) consistent noise effects. Another way to test the home‐voter hypothesis is to compare the votes cast by homeown‐
ers and renters. Homeowners, whose properties are affected by aircraft noise, should – in the presence of the price signals revealed in the section above – be more supportive of the closure of Tempelhof within the noise areas of Tempelhof and Tegel. Similarly, they will have a larger incentive to vote for the continued operation of Tempelhof if their properties are located within the noise protection zone of Schönefeld/BBI. Our findings, presented in columns (5) and (6), where announcement and homeownership effects are estimated jointly, are largely in line with these expectations. There is generally less support for the referendum in precincts with a higher proportion of homeowners, and even more so with‐
in the noise areas of Tegel and Tempelhof. In contrast, support increases in the homeow‐
nership rate within the noise zone of Schönefeld. It has to be noted, however, that the coef‐
ficient of the Tempelhof noise × homeownership interactive term (HR × DN) does not sa‐
tisfy conventional significance criteria. AHLFELDT / MAENNIG – Are renters better voters? 29 Tab. 3 Voting analysis – baseline results East‐Berlin (dummy) Conservatives (proportion) Greens (proportion) Age <18 (proportion) Age 18 to 27 (proportion) Age 45 to 55 (proportion) Age >55 (proportion) Purch. power (1000€/capita) Non‐German (proportion) Tempelhof area noise (db) Tegel area noise (db) Schönefeld zone (dummy) Announcement Effect (
) Announce. × Tempelhof area Announce. × Tegel area Announce. × Schönef. area Homeownership Rate Homeown. rate × Tempelhof area Homeown. rate × Tegel area Homeown. rate × Schönef. area Constant Accessibility Eff. Observations R‐squared (1) ‐27.819** (1.284) 0.626** (0.048) ‐0.959** (0.047) ‐0.589** (0.065) ‐0.943** (0.102) ‐0.777** (0.088) ‐0.593** (0.046) 0.401* (0.201) 0.081* (0.035) (2) (3) (4) (5) (6) ‐29.654** ‐29.684** ‐28.600** ‐27.890** ‐28.261** (1.182) (1.213) (1.345) (1.214) (1.645) 0.643** 0.642** 0.607** 0.727** 0.716** (0.044) (0.045) (0.05) (0.049) (0.051) ‐0.968** ‐0.976** ‐0.969** ‐0.905** ‐0.908** (0.044) (0.044) (0.048) (0.048) (0.049) ‐0.584** ‐0.582** ‐0.569** ‐0.482** ‐0.470** (0.065) (0.063) (0.064) (0.07) (0.066) ‐0.897** ‐0.911** ‐0.936** ‐0.835** ‐0.844** (0.096) (0.096) (0.103) (0.097) (0.103) ‐0.819** ‐0.821** ‐0.751** ‐0.712** ‐0.715** (0.077) (0.078) (0.088) (0.073) (0.086) ‐0.601** ‐0.602** ‐0.591** ‐0.561** ‐0.567** (0.044) (0.043) (0.049) (0.045) (0.052) ‐0.051 ‐0.061 0.237 ‐0.112 ‐0.179 (0.175) (0.177) (0.233) (0.176) (0.211) 0.057+ 0.073* 0.091* 0.036 0.028 (0.033) (0.033) (0.037) (0.031) (0.04) ‐0.943** ‐0.916** ‐0.894** ‐0.533 (0.196) (0.189) (0.196) (0.627) ‐0.229** ‐0.240** ‐0.156** ‐0.137* (0.038) (0.038) (0.037) (0.065) 32.167** 32.185** 1.823 0.296 (5.511) (5.539) (7.017) (176.616) ‐0.06 ‐0.083 (0.096) (0.109) ‐0.274** ‐0.068 (0.102) (0.222) ‐0.261** ‐0.027 (0.094) (0.148) 0.248 0.104 (0.832) (0.951) ‐0.026* ‐0.025* (0.012) (0.012) ‐0.097 ‐0.108 (0.126) (0.54) ‐0.094** ‐0.096** (0.017) (0.017) 0.418** 0.428 (0.092) (2.028) 104.567** 113.654** 114.110** 107.858** 106.804** 108.850** (4.575) (4.336) (4.306) (5.529) (5.141) (7.693) ‐ ‐ YES ‐ ‐ ‐ 1201 1201 1201 1201 1201 1201 0.88 0.91 0.91 0.89 0.91 0.91 Notes: Dependent variable is the share of yes‐votes among total votes in all models. Accessibility effects are a set of dummy variables for 10 mutually exclusive 1‐km road distance rings around the Tempelhof air terminal. Robust standard errors are in parentheses. +/*/** denote statistical significance at the 10/5/1 % levels. AHLFELDT / MAENNIG – Are renters better voters? 30 As laid out in the sub‐section above, we employ an alternative approach to evaluate the treatment heterogeneity of aircraft noise: the separate estimation of equation (6) for 81 sub‐samples of precincts that fall into different 20‐percentage‐point intervals of their ho‐
meownership rate. The estimated noise treatment effects from these separate regressions are depicted in Figure 4. We note that virtually all noise treatment coefficients are esti‐
mated at high levels of statistical significance and that the 20‐percentage‐point band is large enough to ensure that each subsample covers several hundred precincts. Figure 4 reveals a negative effect of noise exposure on the share of yes‐votes for virtually all levels of homeownership both for Tegel as well as Tempelhof air noise. Furthermore, we find a considerable degree of treatment heterogeneity. As expected, the treatment ef‐
fect of noise on the share of yes‐votes in the referendum increases in the rate of homeow‐
nership. For Tempelhof airport, the effect of a 10‐db increase in air noise on the share of yes‐votes varies as much as from about a 5‐percentage‐point reduction for the samples with low homeownership to 15 percentage points for the samples with relatively high homeownership. Notably, homeowners, according to the approximated homeownership rate, are in the minority even in the precincts with the highest proportion of homeowners. The high degree of treatment heterogeneity thus indicates that, at the margin, homeown‐
ers exerted a disproportionate impact on the voting outcome. A considerable degree of treatment heterogeneity can also be found for the Tegel noise impact area, although a remarkable increase in the magnitude of the treatment effect only occurs at higher homeownership levels and heterogeneity is generally smaller than in the Tempelhof area. This is in line with generally lower involvement of voters living near Te‐
gel as a result of their less direct connection to the referendum and the less pronounced influence of homeowners on the voting outcome at the margin. For Schönefeld, too, the magnitude of the treatment effect is considerably higher for the high homeownership rate samples. Apparently, homeowners were particularly concerned with a potential shift of air traffic from Tempelhof (and Tegel) to their neighborhood. Altogether, our findings provide strong support for hypothesis V and the home‐voter hypothesis more generally. AHLFELDT / MAENNIG – Are renters better voters? 31 Fig. 4 Noise treatment heterogeneity Notes: Illustrations visualize treatment coefficients that form a set of type (6) specifications. Lowess trend lines (solid) are fit to the estimated treatment coefficients (dashed). 5 Conclusion This study evaluates the cost of aircraft noise as revealed in property prices and in a direct vote on an airport closure against the background of the home‐voter hypothesis. It in‐
cludes several new findings. First, we investigated real estate price reactions to effective and announced closures and extensions of airports instead of estimating the effect of air‐
craft noise based on a cross‐sectional research design. Using exogenous variation in air‐
port noise helps to keep unobservable location characteristics constant and to better iso‐
late noise effects from correlated location effects. Second, we contrasted the evaluation of an environmental disamenity via property price effects with an analysis of direct votes in a specific referendum. Third, we investigated whether homeowners react more sensitively than renters to the presence of an environmental disamenity. We argue that homeowners have an incentive to overstate their preferences for environmental (dis)amenities in an attempt to increase the value of their properties, whereas the opposite is true for renters, who benefit from lower rents. Our results indicate that aircraft noise is costly. We observe significant positive market adjustments to reductions in aircraft noise, which happened partly when the plans to close Tempelhof airport were first announced and when certainty about the outcome was achieved. Both renters and homeowners exhibited an opposition to the continued opera‐
tion of Tempelhof airport, which increased with the degree to which they were exposed to aircraft noise from Tempelhof airport as well as Tegel, the other inner‐city airport that is scheduled for closure. The opposite effect happened in areas where the expansion of Schönefeld airport to create the new major international airport, BBI, will increase noise AHLFELDT / MAENNIG – Are renters better voters? 32 levels. Notably, significant property price effects are only found for the sample of (owner‐
occupied) single family properties; few effects could be found for multi‐family apartment buildings, which are typically inhabited by renters. The identified “surface” of price adjustments for single family properties, in any case, is consistent with the measured noise pattern, and significantly explains the voting behavior observed in the referendum. This is in line with the home‐voter hypothesis, which predicts increased support by homeowners for initiatives that increase the values of their houses and has recently attracted considerable attention in the literature. The home‐voter hypo‐
thesis is also supported by higher relative support for the closure of Tempelhof airport by homeowners than by renters within precincts that suffer from air noise from the two city airports, Tempelhof and Tegel. Consistently, support for the continued operation of Tem‐
pelhof is higher for homeowners than for renters within the noise impact area of the new major airport at Schönefeld. We note that besides the expected property appreciation unobserved characteristics of homeowners may have contributed to their NIMBY attitude. Among other factors, home‐
owners may be generally more attached to their local area due to higher moving cost, be less dependent on the local jobs offered by the airport and, not least, experience a larger disutility from aircraft noise due to more frequent use of garden space. In any case, results of direct referenda on public initiatives should be interpreted with care when it comes to evaluating (expected) environmental effects. The distinct incentives to engage in political bargaining have important implications for the allocation of public facilities with local characteristics in general, but specifically via processes of direct democracy. In the long run, the result could be a concentration of pub‐
lic facilities with negative local externalities (NIMBY facilities) in areas that are dominated by renters, whereas homeowners would attract facilities with positive local externalities (PIMBY) – an implication that might be worthy of investigation in the future.13 13 The acronyms NIMBY and PIMBY are widely used to describe “not in my backyard!” and “please! In my backyard!” attitudes, respectively. AHLFELDT / MAENNIG – Are renters better voters? 33 Appendix Tab. A1 – Hedonic price effects Floor Space Index (FSI) FSI squared Plot Area (m²) Plot Area squared Story Age (Years) Age (Years) squared Condition: Good Condition: Bad Extra flat in attic Elevator Basement Underground car park Charge for infrastructure Not occupied by renter Share at sec. structure Urban renewal area Dist. to Centre (km) Dist. to Water (km) Dist. to Green (km) Dist. to Station (km) Dist. to Main St. (km) Street Noise (db) Dist. to Industry (km) Landmarks within 600m Dist. to Landmark (km) Dist. to School (km) Precinct Effects Month Effects Daily Trend Year x District Effects Year x CBD Effects Noise Effects Noise x Trend Effects Surface Variables Submarket Period Observations R‐squared (1) Coeff. S.E. 1.931** 0.025 ‐0.538**
0.015 ‐0.000**
0 0.000** 0 0.009* 0.004 ‐0.009**
0 0.000** 0 0.166** 0.005 ‐0.266**
0.007 0.034** 0.004 0.134+ 0.078 0.073** 0.006 ‐0.178* 0.085 ‐0.025**
0.005 0.068** 0.006 ‐1.330**
0.114 ‐0.003 0.004 0.011 0.015 ‐0.019**
0.007 ‐0.036**
0.008 ‐0.065**
0.007 ‐0.01 0.013 ‐0.006**
0 0.024** 0.007 0 0 ‐0.018 0.012 ‐0.044**
0.014 YES YES YES YES (2) Coeff. 0.590** ‐0.030** ‐0.000** 0.000** ‐0.008** ‐0.010** 0.000** 0.499** ‐0.307** 0.189** 0.098** 0.093** 0.114 ‐0.014 0.045* ‐0.033 0.001 ‐0.062** 0.036* ‐0.012 ‐0.009 ‐0.095** 0.002** 0.076** 0.002** ‐0.135** 0.034+ YES YES YES YES S.E. 0.006 0.001 0 0 0.002 0 0 0.01 0.008 0.008 0.013 0.009 0.073 0.014 0.019 0.036 0.003 0.021 0.016 0.014 0.017 0.031 0 0.017 0 0.033 0.019 (3) Coeff. 1.734** ‐0.257** ‐0.000** 0.000** 0.008 ‐0.007** 0.000** 0.144** ‐0.208** ‐0.012 0.644+ 0.031 0 ‐0.037* 0.107** ‐4.225** 0 0.004 ‐0.056 ‐0.046 ‐0.052+ ‐0.033 ‐0.002+ 0.045 0 0.061 ‐0.478* YES YES YES YES S.E. 0.084 0.033 0 0 0.014 0.001 0 0.019 0.022 0.014 0.366 0.021 0 0.019 0.019 0.713 0 0.071 0.034 0.03 0.029 0.049 0.001 0.032 0.001 0.044 0.188 YES YES YES YES YES ‐ Single‐family 1990‐2009 35,098 0.80 YES YES ‐ Multi‐family 1990‐2009 38,991 0.72 ‐ ‐ YES Single‐family 1 YEAR +/‐ ANN. A) 2,906 0.84 Notes: Dependent variable is the log of price per square meter of land in all models. Standard errors are heteroskedasticity robust. **/*/+ denote significance at the 1/5/10% levels. AHLFELDT / MAENNIG – Are renters better voters? 34 Tab. A2 Voting analysis – robustness checks East Berlin (dummy) Conservatives (proportion) Greens (proportion) Age <18 (proportion) Age 18 to 27 (proportion) Age 45 to 55 (proportion) Age >55 (proportion) Purch. power (1000€/capita) Non‐German (proportion) Tempelhof noise (db) Tegel noise (db) Schönefeld zone (dummy) Constant Lambda ( ) Observations (Pseudo) R‐sq. (1) OLS Yes/Total ‐27.604** (0.895) 0.564** (0.029) ‐0.514** (0.033) ‐0.872** (0.192) ‐0.092* (0.041) 31.291** (5.072) 58.885** (1.371) 1201 0.88 (2) OLS Turnout ‐4.389** (0.723) 0.251** (0.029) 0.299** (0.03) 0.039 (0.043) ‐0.255** (0.066) 0.003 (0.044) 0.068* (0.031) 0.231* (0.109) ‐0.245** (0.024) 0.670** (0.074) ‐0.111** (0.03) 10.239** (2.542) 17.697** (2.893) 1201 0.74 (3) OLS No/Elect. 5.324** (0.422) ‐0.114** (0.017) 0.363** (0.018) 0.157** (0.025) ‐0.025 (0.043) 0.237** (0.033) 0.164** (0.019) 0.156* (0.065) ‐0.079** (0.012) 0.589** (0.074) 0.074** (0.016) ‐5.724** (1.399) ‐5.458** (1.747) 1201 0.67 (4) (5) (6) OLS WLS SAR Yes/Elect. Yes/Total Yes/Total ‐9.712** ‐29.203** ‐28.331** (0.612) (1.003) (1.271) 0.365** 0.713** 0.441** (0.023) (0.042) (0.036) ‐0.065** ‐0.962** ‐0.764** (0.023) (0.046) (0.044) ‐0.117** ‐0.645** ‐0.322** (0.032) (0.071) (0.066) ‐0.231** ‐0.940** ‐0.127 (0.047) (0.097) (0.093) ‐0.234** ‐0.796** ‐0.413** (0.035) (0.067) (0.079) ‐0.097** ‐0.642** ‐0.285*** (0.022) (0.046) (0.042) 0.074 ‐0.309+ 0 (0.088) (0.166) (0) ‐0.167** 0.045 0.015 (0.02) (0.031) (0.036) 0.08 ‐0.947** ‐0.606** (0.083) (0.122) (0.170) ‐0.185** ‐0.234** ‐0.197* (0.023) (0.043) (0.095) 15.942** 31.910** 6.043* (3.624) (2.411) (2.481) 23.130** 118.328** 86.504** (2.293) (4.426) (4.987) 8.821*** 1201 1201 1201 0.88 0.92 0.95 Notes: Dependent variable is the share of yes‐votes among total votes in (1) and (5‐6), turnout in (2), share of no‐votes among the total electorate (3), or share of yes‐votes among the total electorate (4). Robust standard errors are in parentheses. +/*/** denote statistical significance at the 10/5/1 % levels. AHLFELDT / MAENNIG – Are renters better voters? 35 Tab . A3 Voting Results – Robustness Checks II THF noise >45 db (Dummy) TXL noise > 45 db (Dummy) Schönefeld zone (dummy) Constant Basic controls Socio‐economic controls Accessibility Eff. Observations R‐squared (1) ‐8.391** (1.291) ‐0.708 (0.487) 31.285** (5.069) 58.807** (1.352) YES (2) ‐8.426** (1.039) ‐2.614** (0.449) 31.996** (5.504) 113.477** (4.302) YES YES 1,201 0.88 1,201 0.91 (3) ‐8.129** (1.037) ‐2.776** (0.458) 32.024** (5.53) 113.930** (4.288) YES YES YES 1,201 0.91 Notes: Dependent variable is the log of price per square meter of land in all models. Basic con‐
trols, socio‐economic controls and Accessibility effects defined as in Table 3. Standard er‐
rors are heteroskedasticity robust. **/*/+ denote significance at the 1/5/10% levels. AHLFELDT / MAENNIG – Are renters better voters? 36 Literature Ahlfeldt, G. M. (2010a). Blessing or Curse? Appreciation, amenities and resistance to urban renewal. Regional Science and Urban Economics, DOI:10.1016/j.regsciurbeco.2010.07.006. Ahlfeldt, G. M. (2010b). If Alonso was right: Modeling Accessibility and Explaining the Res‐
idential Land Gradient. Journal of Regional Science, DOI: 10.1111/j.1467‐
9787.2010.00694.x. Ahlfeldt, G. M. (in press). The Train has Left the Station: Do Markets Value Inter‐City Access to Intra‐City Rail Lines? German Economic Review, forthcoming. Ahlfeldt, G. M., & Maennig, W. (2010). External utility and productivity effects of city air‐
ports. Working Paper. Ahlfeldt, G. M., & Schrayvogel, F. (2010). Wahrgenommene Kosten und Nutzen innerstädtischer Flughäfen. Zeitschrift für amtliche Statistik Berlin Brandenburg, 2010(2), 24‐26. Ahlfeldt, G. M., & Wendland, N. (2009). Looming Stations: Valuing Transport Innovations in Historical Context Economics Letters, 105(1), 97‐99. Angrist, J. D., & Pischke, J.‐S. (2009). Mostly Harmless Econometrics: An Empiricist's Compa‐
nion: Princton University Press. Anselin, L. (1995). Simple Diagnostic Tests for Spatial Dependence. Urbana‐Champaign. Arntz, M., & Wilke, R. A. (2007). An application of cartographic area interpolation to Ger‐
man administrative data. Advances in Statistical Analysis, 91(2), 159‐180. Brandt, S., & Maennig, W. (in press). Road noise exposure and residential property prices: Evidence from Hamburg Transportation Research Part D. Brunner, E., & Sonstelie, J. (2003). Homeowners, property values, and the political econo‐
my of the school voucher. Journal of Urban Economics, 54(2), 239‐257. Brunner, E., Sonstelie, J., & Thayer, M. (2001). Capitalization and the Voucher: An Analysis of Precinct Returns from California's Proposition 174. Journal of Urban Economics, 50(3), 517‐536. Collins, A., & Evans, A. (1994). Aircraft Noise and Residential Property Values: An Artificial Neural Network Approach. Journal of Transport Economics and Policy, 28(2), 175‐
197. Dachis, B. D. I., Duranton, G., & Turner, M. (2009). The effects of land transfer taxes on real estate markets: Evidence from a natural experiment in Toronto. University of To‐
ronto Discussion Paper. Dehring, C. A., Depken, C. A., & Ward, M. R. (2008). A direct test of the homevoter hypothe‐
sis. Journal of Urban Economics, 64(1), 155‐170. Feinerman, E., Finkelshtain, I., & Kan, I. (2004). On A Political Solution to the NIMBY Con‐
flict. American Economic Review, 94(1), 369‐381. Fischel, F. A. (2001). The Homevoter Hypothesis (Vol. Cambridge, MA.): Harvard Univ. Press. Frey, B. S., Oberholzer‐Gee, F., & Eichenberger, R. (1996). The old lady visits your back‐
yard: A tale of morals and markets. Journal of Political Economy, 104(6), 1297. Gibbons, S., & Machin, S. (2005). Valuing rail access using transport innovations. Journal of Urban Economics, 57(1), 148‐169. Goodchild, M. F., & Lam, N. S. (1980). Areal Interpolation: a variant of the traditional spa‐
tial problem. Geo‐Processing, 1, 297‐312. Hilber, C. A. L., & Mayer, C. J. (2009). Why Do Households Without Children Support Local Public Schools? Journal of Urban Economics, 65(1), 74‐90. Jenks, G. F. (1977). Optimal data classification for choropleth maps, Occasional Paper, No. 2. Kansas: Lawrence. McMillen, D. P., & McDonald, J. F. (2004). Reaction of House Prices to a New Rapid Transit Line: Chicago's Midway Line, 1983‐1999. Real Estate Economics, 32(3), 463‐486. Mieszkowski, P., & Saper, A. M. (1978). An Estimate of the Effects of Airport Noise on Property Values. [Article]. Journal of Urban Economics, 5(4), 425‐440. Murphy, K. M., & Topel, R. H. (2002). Estimation and Inference in Two‐Step Econometric Models. Journal of Business & Economics Statistics, 20(1), 88‐97. Nelson, J. P. (1979). Airport Noise, Location Rent, and the Market for Residential Ameni‐
ties. Journal of Environmental Economics and Management, 6(4), 320‐331. AHLFELDT / MAENNIG – Are renters better voters? 37 Nelson, J. P. (1980). Airports and Property Values: A Survey of Recent Evidence. Journal of Transport Economics and Policy, 14(1), 37‐52. Nelson, J. P. (2004). Meta‐Analysis of Airport Noise and Hedonic Property Values: Prob‐
lems and Prospects. Journal of Transport Economics & Policy, 38(1), 1‐28. Nitsch, V. (2009). Fly or Cry: Is Airport Noise Costly? CESifo Working Paper Series, 2732, 1‐
30. Pennington, G., Topham, N., & Ward, R. (1990). Aircraft Noise and Residential Property Values Adjacent to Manchester International Airport. Journal of Transport Econom‐
ics and Policy, 24(1), 49‐59. Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy, 82(1), 34‐55. Schipper, Y., Nijkamp, P., & Rietveld, P. (2002). Thirty years of aircraft noise value studies: a meta‐analysis. In R. J. G. M. Florax, P. Nijkamp & K. G. Willis (Eds.), Comparative environmental economic assessment. Cheltenham: Elgar. Senatsverwaltung für Stadtentwicklung Berlin. (2006). Urban and Environmental Information System. Berlin. Tomkins, J., Topham, N., Twomey, J., & Ward, R. (1998). Noise versus access: The impact of an airport in an urban property market. [Article]. Urban Studies, 35(2), 243‐258. Uyeno, D., Hamilton, S. W., & Biggs, A. J. G. (1993). Density of Residential Land Use and the Impact of Airport Noise. Journal of Transport Economics and Policy, 27(1), 3‐18. Van Praag, B. M. S., & Baarsma, B. E. (2005). Using Happiness Surveys to Value Intangibles: The Case of Airport Noise. [Article]. Economic Journal, 115(500), 224‐246. Voith, R. (1991). Transportation, Sorting and House Values. American Real Estate and Ur‐
ban Economics Association Journal, 19(2), 117‐137. Spatial Economics Research Centre (SERC)
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