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01-09-2012

The impact of rail access on condominium prices in Hamburg

Authors: Sebastian Brandt, Wolfgang Maennig

Published in: Transportation | Issue 5/2012

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Abstract

Using hedonic price functions, we study the influence of access to public railway stations on the prices of surrounding condominiums in Hamburg, Germany. The study examines the influence of rail infrastructure on residential property prices, not only of individual lines, but for the entire rail network of a metropolitan region. We test the stability of the coefficients for different sets of control variables. The study also estimates public-transit-induced increases in tax revenues due to real estate price increases for a study area outside the United States. We control for spatial dependence and numerous variables correlated with the proximity of railway stations and show that access to the public transit system of the city of Hamburg is to be rated with price increases of up to 4.6%. Such premiums for higher-income neighbourhoods and for subterranean stations tend to be higher. The premiums calculated are significantly lower than average price premiums reported in previous studies, which were mostly based on much fewer variables that rail access might be correlated to.

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The companies in the linked transport system in the city of Hamburg (HVV) investigated in this study have recently generated 71% of their costs (Gassdorf 2007).

We owe this to an anonymous referee.

Löchl and Axhausen (2010) also control for the distance to railway stations and provide sensitivity tests by OLS, spatial autoregressive, and geographically weighted regression (GWR) techniques.

In fact, in Germany a Committee of Valuation Experts that collects sales prices of housing units is located in every county. But in practice strict data protection regulations and high fees make it difficult to get access to detailed datasets of actual sales prices containing information on property’s addresses.

In the regression that uses the sales price as a dependent variable, a tiled roof, as opposed to an iron roof, is valuated at A$4,800, while the regression where the offer price is the dependent variable arrives at a price premium of A$6,300. In addition, the coefficient of SIZE calculated on the basis of the offer prices exceeds the coefficient calculated on the basis of the sales prices by approximately 20%.

Grether and Mieszkowski (1974) also note that it is reasonable to assume that missing information on property characteristics, which may be connected to the use of offer data, does not give rise to a systematic bias of coefficients.

Cf. F+B (2002). To our knowledge, there have not been any further studies on the influence that property characteristics have on the difference between offer and transaction prices.

By contrast, the linear form produces coefficients that represent absolute changes in property prices for an additional unit of a property characteristic. Since listing prices are systematically higher than sales prices, coefficients obtained from linear functional forms using listing prices as the dependent variable are likely to overestimate the effects on housing prices examined, independently from whether the difference between listing and transaction prices is correlated with the physical characteristics of a condominium or not.

Initially the service provider IDN ImmoDaten GmbH extracted the data from the portals automatically. Subsequently, the data were adjusted by IDN and F+B to remove duplications and implausible datasets.

The distribution of the condominiums examined across the area of Hamburg can be seen in Fig. 1.

All population data refer to the year in which the property was offered for sale most recently. The information regarding average income, however, was available only for 1995.

Determinants for the choice of means of transport, for example, have been described by Schwanen and Mokhtarian (2005) as well as Simma and Axhausen (2003).

This leaves the number of stops and lines in the area of the city of Hamburg constant over the study period.

We thank an anonymous referee for pointing out that Rosen (1974) suggests a two-step approach where the hedonic (the first stage) is used in a second stage to determine demand functions for housing characteristics. This study limits the analysis to a study of hedonic prices.

Descriptive statistics of the variables included in the final model specifications are listed in Table 1.

Since Sirmans et al. (2005) and Wilhelmsson (2000) primarily used studies on U.S. housing markets in their analysis, it seemed meaningful for an analysis of a German market to differ in some respects. Given that Hamburg in Northern Germany has a moderate climate even in the summer, which essentially negates the use of air-conditioning for residential property, we have decided to drop this control variable. In contrast to the North-American housing markets, which are dominated mostly by single-family homes, the characteristics BALCONY and KITCHEN can have a considerable impact on the value of German condominiums.

In preliminary studies, we have further tested whether a change in the population (in the statistical district or the urban district) over the study period had any influence on condo prices. Since a significant effect had not been observed for any of the specifications tested, we decided to leave this aspect out of the final model specifications.

In order to avoid overestimation of Emp_j and/or Emp_i, we did not allow d _ij and/or d _ii to take on values smaller than 1. The regression coefficient of the gravity variable calculated from the graded weights shows a higher t-value than the coefficient of the variable calculated from nongraded weights.

Numerous studies have observed that part of the variability in property prices can be explained by the distance to the nearest school (e.g., Agostini and Palmucci 2008). However, as preliminary regressions did not yield significant coefficients for either linear or additional quadratic distance terms, we have excluded the distance to schools from the final model specifications.

Other studies frequently use the distance to the rail tracks as an indicator of noise exposure (e.g., Strand and Vågnes 2001). However, shielding effects (e.g., because of the topography, noise barriers or the surrounding buildings) result in very different levels of noise pollution and visual nuisance for an identical distance to railway tracks.

Can and Megbolugbe (1997) consider properties within a radius of 3 km. However, their study area covers a large-area suburban county in the metropolitan region of Miami. As concerns the small-scale housing market in Hamburg, it is reasonable to assume that the offer price of a condominium is affected only by prices of properties that are located in the immediate vicinity. However, we computed AUTOREG using various critical distances (0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.5 and 10.0 km) and found the best fit of the model when we considered properties within a radius of 2 km. In contrast to Can and Megbolugbe (1997), who take into account surrounding properties if they were sold in the previous 6 months, we believe, given the relatively low volatility of the condominium market in Hamburg, that it is reasonable to include properties in the neighbourhood that were offered for sale within the previous 12 months.

Preliminary regressions have shown that other station characteristics, contrary to some observations such as by Gibbons and Machin (2005), do not affect the structure of condo prices in Hamburg. These include frequency of service, number of serving railway, whether the nearest station has a parking lot or whether it is a transfer station. Furthermore, it has no bearing on whether the next station is part of the light rail or commuter rail system. We have also examined the effect of crime density and frequency on the surrounding property prices. Crime data, however, were only made available at the neighbourhood level and yielded insignificant results.

We identify neighbourhoods with incomes above the median by splitting our random sample into two sub-samples of equal size on the basis of the median of the variable INCOME.

If the models are specified without the spatial lag term, the adjusted R ² value is reduced by approximately 1.0%.

Following Halvorsen and Palmquist (1980), the coefficients of dummy variables used in the semi-log form were transformed by (e ^a − 1), where a is the OLS coefficient.

Since for both models the results are independent of whether the lag term is included or not, we do not adjust our estimates for spatial correlation (Andersson et al. 2009).

We owe this comment to an anonymous referee.

Debrezion et al. (2006) only consider the distance to railway tracks and the nearest motorway junction, McMillen and McDonald (2004) control for the distance to railway tracks and to CBD, and Grass (1992) only considers the distance to the CBD.

Both the district data on average sales prices and the number of residential units per type of use were obtained from the Statistics Office of Hamburg (2009). We use the sales price data because our sample does not contain offer prices for all urban districts. We differentiate the types of use according to condominiums as well as single- and two-family houses.

The method presented here implies some assumptions: In addition to an unsystematic distribution of residential and commercial properties over the area of the city of Hamburg, the transferability of the premiums for condominiums is also assumed for single- and two-family homes. Against the background of the aforementioned polycentric distribution of jobs, it is likely that the first assumption has been met at least approximately. Potential biases due to the transfer of the results for condominiums on to single- and two-family houses are minimised not least by the fact that single- and two-family houses account for only about 21% of all residential units in Hamburg (Statistikamt 2009).

Furthermore, good access to train stations may also increase the rent of a residential property (e.g., Benjamin and Sirmans 1996) and thus the taxable income of the landlord.

We owe this idea to an anonymous referee.

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Title: The impact of rail access on condominium prices in Hamburg
Authors: Sebastian Brandt
Wolfgang Maennig
Publication date: 01-09-2012
Publisher: Springer US
Published in: Transportation / Issue 5/2012
Print ISSN: 0049-4488
Electronic ISSN: 1572-9435
DOI: https://doi.org/10.1007/s11116-011-9379-0

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