The influence of noise on net revenue and values of investment properties: Evidence from Switzerland

In this study we use hedonic models to measure the influence of noise nuisance on rents, costs and values of investment properties in Switzerland. Countrywide data is provided by institutional real estate investors. The effects are measured for aircraft noise, road traffic noise and railroad noise. We show that negative effects appear between lower and upper tresholds which vary between different noise types and across residential and non-residential properties. Rents, costs and values are affected below the administrative tresholds given by the LSV and the negative impact ceases at an upper threshold. However high noise nuisance might influence investment decisions, i.e. offices are built instead of housing etc. These important effects are not given account in the data. In addition, directly measured reductions on market values are lower than the expected reductions based on empirical effects on rents and costs. The reasons for the different market value reductions may be found in the Swiss tenancy law. Rents for dwellings within existing rental agreements can only be adjusted in accordance with the change of the “reference interest rate” (Referenzzinssatz) and the CPI. The analysis shows that the average contract duration is dependent on the noise nuisance, which leads to a significant reduction of noise-induced losses within periods of increasing market rents. ∗ Baumhaldenstrasse 34, 8055 Zürich, Switzerland, +41 79 707 85 27; sf@fpre.ch. ** Departement VWL, University of Bern, michael.gerfin@vwi.unibe.ch. *** Habsburgstrasse 39, 8037 Zürich, Switzerland, +41 79 637 93 57; ml@fpre.ch. This research was made by appointment and financial support of the Swiss Federal Office for the Environment FOEN. Special thanks go to the data providing real estate owners/managers namely Adimmo, Allianz Versicherung, Allreal, BVK Kanton Zürich, Fundamenta Group, Migros Pensionskasse, Intershop, Pensimo Management, Swisscanto Asset Management, Swiss Life, UBS Fund Management. This paper benefited from helpful comments by Dominik Matter. The usual disclaimer applies. First draft: April 2014. Drafts have been presented to the FOEN and to various institutional real estate owners/managers.


INTRODUCTION
In Switzerland, road and rail traffic as well as aircraft noise are important sources of nuisance in settlement areas. The fact that real estate markets value traffic noise has been shown by many different empirical studies, e.g. Andersson et al. (2009), Day et al. (2007) and Kim et al. (2007). Nelson (2008) published a meta analysis of studies assessing the impacts of aircraft and road traffic noise. Most of the existing studies explore noise effects on prices of private properties and market rents for apartment.
So far, there is little knowledge on the effect of noise on investment properties. This part of the building stock contains multi-family houses as well as office buildings, shopping malls, mixed-used properties and others. With a house owner quota of only about 40 per cent, the major part of Swiss households rents a flat. In addition to the general importance of the rental market, the question of the impact of noise on investment properties becomes important because of deadlines for noise remediation. In a couple of years Cantons and railway companies will have to compensate house owners for losses due to excessive noise nuisance. 1 Today there is only compensation for private properties and multi-family houses affected by aircraft noise around Zurich airport.
Estimating hedonic models for investment properties is a challenge, since noise nuisance can affect market rents, contract rents and owner side costs as well as risk assessments (discounting factors in DCF appraisal). In addition there is no database with detailed and harmonised transaction data. For this study a uniquely large and well-described dataset of institutional properties has been compiled. It contains comparable information across all appraisal-relevant components of investment properties as well as the market values of these properties.
This study is based on the theory that noise affects both the gross revenue (reduction of rental income) as well as the owner-side costs (increased owner costs due to higher fluctuation, vacancies and maintenance costs). With the available data, noise effects can be measured on both the gross revenue as well as the owner-side costs. In addition, the data allow estimating the influence of noise nuisance directly on the market values. 1 According to the federal "Lärmschutzverordnung" LSV (Bundeskanzlei, 1986), the trigger for compensation is average noise dB(A) above the "Immissionsgrenzwert" IGW. These IGW differ by planning zones, noise source and between day and night. Stefan S. Fahrländer, Michael Gerfin and Manuel Lehner The influence of noise on net revenue and values of investment properties investment properties: Evidence from Switzerland 3 In Switzerland, several studies estimating the influence of noise nuisance on market rents for apartments exist (for an overview see Table 1 and Fahrländer Partner, 2013). One single study measures the influence of aircraft noise on values of investment properties (see Bundesgericht, 2011). The observed reductions of the market values of around 1.5% per dB(A) are significantly higher than the measured reductions on apartment rents of approximatly 0.3% per dB(A). This supports the hypothesis formulated above that noise not only causes losses at the income side, but also leads to higher costs and higher risks for the owner.  In general, it can be stated that the samples are well distributed over the country (see Figure   1). An obvious concentration of observations exists in the urban areas with a significant rental market.  represents average noise levels dB(A) for the period 0600 to 2200 hours (day) and 2200 to 0600 hours (night).

MODELS AND RESULTS
To select the model variables, this study relies on Sirmans et al. (2005), Malpezzi (2002) and Wilhelmsson (2000) who evaluated the control variables which are most commonly used in hedonic studies. In a first step (section 3.1), impacts of different noise sources on different property types are expolored using nonparametric cubic splines (as shown in Fahrländer, 2006) in generalized additive models (Hastie & Tibshirani, 1990). Minimum thresholds of noise effects were detected in all cases, maximum limits only in some.
In a second step, log-linear hedonic models are developed to measure noise impacts on rents (section 3.2), owner costs (3.3) and market values (3.4) using OLS regressions. All models include fixed effects (macro-location price indicators) derived from the hedonic models of Fahrländer Partner (Fahrländer, 2006). In a third step, the empirically measured reductions on market values are compared to indirect reductions resulting from additional costs and reduced rents (3.5).

EXPLORATORY ANALYSIS OF NOISE IMPACT
To explore noise impacts, all the parameters describing the micro-location must be used to isolate the influence of noise nuisance. This can only be done with highly dissaggregated data representing the small-scale conditions at a certain address. For the explorative analysis of the impact of noise a generalized additive model with cubic regression splines is used to analyse the pattern of the impact of the different noise sources and levels on rents, costs and values.
Since noise from different sources cannot be combined, every single noise source is tested seperatly.
The objective of these estimations is to find adequate thresholds for all models. The determination of the thresholds was performed manually for each combination of noise source and property type using spline plots as shown in Figure Table 3 shows the findings of the exploratory analysis. In the apartment rents model we found a maximum thresholds of noise impact at 57dB (aircraft noise) and 55dB (road and rail noise), the minimum and maximum thresholds are shown in the row "range". Apartment rents and market values of residential properties are sensitive to noise during the nights while office and retail rents are affected by daytime noise.

NOISE IMPACT ON CONTRACT RENTS
Two different models have been estimated explaining the contractual rents of apartments.
Both models are based on equation (1) where ߚ represent the coefficients of contiuous and dummy variables and ߚ መ vectors of coefficients of factor variables and interaction terms. The noise interaction terms include a RangeDummy to separate the effects within the lower and upper thresholds.
The first model does not include the spatial-type-interactions for the noise variables but country-wide coefficents for noise. All noise coefficients in this model turn out with a highly significant and negative impact. The second model includes interaction terms for different spatial types for road traffic noise and rail noise, as shown in Table 4. 5 The strongest price impact is found in rich communes (type 4), where each decibel road traffic noise above the threshold causes a rent decrease of approximately 0.33%. In suburban residental communes (types 5 and 6) the decrease is less (0.15% and 0.25% per decibel) but also highly significant.
Apartment rents in big cities (type 1) and regional centres (type 2) are not significantly sensitive to road traffic noise. The rail noise coefficents are more difficult to estimate due to fewer observations with excessive rail noise. Significant coefficients can be estimated for large cities and residential communes of regional centres, where rail noise clearly causes lower apartment rents.  Similar models are estimated for office and retail rental units as well as for restaurants. In the models for offices, significant negative coefficents can be estimated only in rich communes (type 4, see Table 5). Estimations for retail contract rents and restaurants do not generate significant coefficients. These models are therefore not subject to further analysis in this article.

NOISE IMPACT ON OWNER-SIDE COSTS
This model includes data of the owner-side running costs. Since the various cost categories cannot be consistenty harmonised for the different data providers, this model is only estimated for the total annual running costs per square meter floor area, as shown in equation (2). The noise interaction terms include a RangeDummy to separate the effects within the lower and upper thresholds.
The results of the estimation suggest that a positive interrelation between noise and ownerside costs exists (see Table 6). However, only the coefficient of the aircraft noise is statistically significant. The result can be interpreted as follows: each dB aircraft noise above 50dB causes 0.88% additional owner-side running costs.

NOISE IMPACT ON MARKET VALUES
Two models were estimated to assess noise impacts on market values. Both models are based on equation (3). The noise interaction terms include a RangeDummy to separate the effects within the lower and upper thresholds.
The first model shows the influence of the explanatory variables on all properties where no spatial or typological distinction of the properties is made. This model confirms the expected relation beween noise and market values (see Table 7). The general negative noise effect on market values of investment properties can therefore be confirmed from an empirical perspective. In the second model, the noise effect is differentiated according to property types.
The estimation shows that market values of pure residential properties ("Residential") and residential properties with additional utilizations ("Residential +") are signifcantly affected by all three types of noise. For office and retail properties, a similar effect can not be shown.
However, a negative noise effect is indicated by the negative coefficients.

DIRECT AND INDIRECT NOISE IMPACT ON MARKET VALUES
As shown above, we have developed statistical models to quantify the noise impact on revenues and costs of investment properties. In addition, a model is available to estimate the influence of noise on market values. These models now allow to compute the value reduction of properties at a given noise exposure in two ways: -Apply noise coefficients from the market value model to calculate the value reduction.
-Apply noise coefficients of the income and cost models to calculate the reduced net income. Then capitalize the reduced net income to calculate the value reduction.
We apply these two calculation methods to a typical residential property from the sample of this study. The property contains 40 apartments and generates CHF 600'000 net annual rental income. At 55dB aircraft noise, a value reduction of about 6.9% is expected due to the reduction of net rents, increased costs and higher risks (see Table 8). By contrast, the estimated reduction is only 2.0% when using the market value model. This large difference is surprising because one would expect more or less the same market value reductions from the two calculation methods. 6 In the example, the net income is capitalized and therefore considered perpetual. In today's appraisals for investment properties the discounted cashflow method (DCF) is widely used. In DCF models, the assumptions about revenues and costs are not constant, but depending on market conditions and the property itself. A lower estimate for income potential of noise-affected properties is expected than for non-noise-exposured properties. In addition, higher costs and vacancies would probably be assumed. The direct reduction of market values would therefore be stronger than in this simple capitalization of the value components. The empirical results show the contrary (for discussion see section 4.2).

THRESHOLDS AND COEFFICIENTS
As shown in section one, most of the existing studies use the "IGW" as a threshold to quantify noise effects on rents and prices. In this study we show that the different noise types have different thresholds that also differ from the thresholds given by the LSV. Thresholds also vary across residential and non-residential properties. In our tests, this leads to different coefficients in comparison to IGW-based models even if we use identical data. Figure 3 shows schematically how the choice of the threshold affects the noise influence for residential rents using rail noise data. The higher the threshold is set, the greater the discount will be.
This example illustrates that the IGW-based coefficients poorly estimate the actual noise impact whereas the coefficient estimated with the lower -empirical -threshold is accurate. In addition, the effect at a high noise level is overestimated in a model using only a lower threshold since data suggest the use of an additional upper threshold is necessary. It has to be assumed that existing Swiss studies using IGW-based thresholds are inaccurate. 6 Since appraisals usually also consider potential rents instead of contract rents i.e. the re-rental to a market rent in the future, the directly at the market value measured reduction should even be bigger than the one calulation with the net capitalization model.

SWISS TENANCY LAW AND AVERAGE RENTAL PERIOD
The reasons for the different market value reductions (as shown in section 3.5) may be found in the Swiss tenancy law. Rents for dwellings within existing rental agreements can only be adjusted in accordance with the change of the "reference interest rate" (Referenzzinssatz) and the consumer price index CPI. In case of a change of tenant, the rent can be adjusted to the market level. Typically, in an investment property the rental income is a mixture between older, indexed rents, and newer rents which are closer to the current market level. The rents observed in this study are therefore a mixture and they have -in a market with rising market rents for around 15 years -increased stronger than the reference interest rate and the CPI. It must therefore be assumed that the net income and thus the market value of a property increases with a higher tenant turnover. A proxy for tenant turnover is the average rental period within a property. The analysis of the available data shows that the average contract duration is also dependent on the noise nuisance, at least for aircraft and rail noise (see Figure   4). 7 Therfore, it is reasonable to assume that a tenant moves after a shorter period of time when he lives in a noise affected apartment compared to a situation without noise nuisance.
With every change of tenant, the owner has the possibility to adjust the rent to the market level. Therefore the Swiss tenancy law may have the side effect of reducing noise-induced losses on gross revenue within periods of increasing market rents. 7 Apartments with a high nuisance of road traffic noise are typically in the big cities, where market situation is extremely tense, expecially in the lower price segments.

NOISE AND INVESTMENT DECISIONS
In this study, the influence of noise on values and value components of investment properties is analysed. Contracts of existing apartments, offices and retail spaces are used as empirical objects of investigation. What can not be examined, however, is the influence of noise on investment decisions. We assume -and this was also confirmed in interviews with several players in the market -that investors, developers and landowners optimise properties within the existing law considering noise influences. For example, in some cases apartments are not built on the lower floors near heavily traveled roads, although it would be permitted in the corresponding zone and it would -if there were no noise -yield higher rental incoms than other utilitsations. In extreme cases, entire buildings with offices, retail spaces or industrial uses are implemented as «noise catchers» in order to create profitable residential uses in other parts of the building lot. The noise exposure leads, in such cases, already at the point of investment decision to a reduced value of the property. We further asume that long term strategies on renovation or repositioning of existing properties are affected by the noise as well. An excellent example of this behaviour can be observed at the Weststrasse in Zurich: In 2010, a massive reduction in road noise was achieved by a major traffic planning project (Kanton Zürich, 2011). In the decades before, only little investment was made anlong this road and the buildings were mostly inhabited by housholds with low incomes. Since the end of the project, major investments by the owners of the buildings were done and the social structure of households has already changed significantly.

CONCLUDING REMARKS
This study quantifies the impact of noise nuisance on rents, costs and values of investment properties. We assume that this is only possible in the range of medium noise. The coefficients are probably only reliable in relatively homogeneous noise situations, since the study is based on averaged day and night values. In extreme situations (i.e. strong aircraft noise in the early morning) the actual price impacts are likely to be higher. Strong noise nuisance most likely affects investment decisions and the effects can therefore not be observed empirically. To do so, it would be necessary to assess the highest and best use for each property with the assumption that there was no noise pollution.
The data used in this study represent the last few years, a period marked by rising rents and tight supply. The measured noise coefficients are valid for this period and can vary with changing market conditions. We suspect that apartment seekers cannot fully cover their preferences (i.e. noise sensitivity) in the current market environment. Furthermore, there is evidence that noise sensitivity of people varies greatly due to the genetic predisposition.
This study does not allow any conclusions about the effects of noise on privately owned residential properties. There, the impacts may be different than in the investment property sector.