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The impact of rail access on condominium prices in Hamburg

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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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Notes

  1. 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).

  2. We owe this to an anonymous referee.

  3. 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.

  4. 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.

  5. 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%.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

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

  11. 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.

  12. 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).

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

  14. 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.

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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

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

  25. 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.

  26. 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).

  27. We owe this comment to an anonymous referee.

  28. 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.

  29. 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.

  30. 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).

  31. 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.

  32. We owe this idea to an anonymous referee.

References

  • Agostini, C.A., Palmucci, G.A.: The anticipated capitalisation effect of a new metro line on housing prices. Fiscal Stud. 29, 233–256 (2008)

    Article  Google Scholar 

  • Ahlfeldt, G.M., Maennig, W.: Substitutability and complementarity of urban amenities: external effects of built heritage in Berlin. Real Estate Econ. 38(2), 285–323 (2010)

    Article  Google Scholar 

  • Alonso, W.: Location and Land Use: Toward a General Theory of Land Rent. Harvard University Press, Cambridge (1964)

    Google Scholar 

  • Andersson, D.E., Shyr, O.F., Fu, J.: Does high-speed rail accessibility influence residential property prices? Hedonic estimates from southern Taiwan. J. Transp. Geogr. 18, 166–174 (2010)

    Article  Google Scholar 

  • Andersson, H., Jonsson, L., Ögren, M.: Property prices and exposure to multiple noise sources: hedonic regression with road and railway noise. Environ. Resour. Econ. (2009). doi:10.1007/s10640-009-9306-4

  • Armstrong, R.J., Rodríguez, D.A.: An evaluation of the accessibility benefits of commuter rail in eastern Massachusetts using spatial hedonic price functions. Transportation 33, 21–43 (2006)

    Article  Google Scholar 

  • Baum-Snow, N., Kahn, M.E.: The effects of new public projects to expand urban rail transit. J. Public Econ. 77, 241–263 (2000)

    Article  Google Scholar 

  • Benjamin, J.D., Sirmans, G.S.: Mass transportation, apartment rent and property values. J. Real Estate Res. 12, 1–8 (1996)

    Article  Google Scholar 

  • Bowes, D.R., Ihlanfeldt, K.R.: Identifying the impacts of rail transit stations on residential property values. J. Urban Econ. 50, 1–25 (2001)

    Article  Google Scholar 

  • BMVBW: Bericht der Bundesregierung über den Öffentlichen Personennahverkehr in Deutschland nach der Vollendung der deutschen Einheit. Bundesministerium für Verkehr, Bau- und Wohnungswesen, Berlin (1999)

    Google Scholar 

  • Box, G.E.P., Cox, D.R.: An analysis of transformations. J. R. Stat. Soc. B 26, 211–252 (1964)

    Google Scholar 

  • BSU: Fahrgeschwindigkeiten im Straßenverkehr – Hamburg vorn im Städtevergleich. Behörde für Stadtentwicklung und Umwelt, Hamburg (2001)

    Google Scholar 

  • BSU: Zentren: Zentrale Standorte nach Flächennutzungsplan und Bestand der Nahversorgungszentren. Behörde für Stadtentwicklung und Umwelt, Hamburg (2003)

    Google Scholar 

  • Can, A., Megbolugbe, I.: Spatial dependence and house price index construction. J. Real Estate Finance Econ. 14, 203–222 (1997)

    Article  Google Scholar 

  • Cassel, E., Mendelsohn, R.: The choice of functional forms for hedonic price equations: comment. J. Urban Econ. 18, 135–142 (1985)

    Article  Google Scholar 

  • Cervero, R., Duncan, M.: Land value impacts of rail transit services in Los Angeles County. Report prepared for National Association of Realtors & Urban Land Institute. Washington, DC (2002)

  • Committee of Valuation Experts in Hamburg: Grundstücksmarktbericht 2007. Gutachterausschuss für Grundstückswerte in Hamburg, Hamburg (2008)

    Google Scholar 

  • Crafts, N.: Market potential in British regions, 1871–1931. Reg. Stud. 39, 1159–1166 (2005)

    Article  Google Scholar 

  • Cropper, M.L., Deck, L.B., McConnell, K.E.: On the choice of functional form for hedonic price functions. Rev. Econ. Stat. 70, 668–675 (1988)

    Article  Google Scholar 

  • Debrezion, G., Pels, E., Rietveld, P.: The impact of rail transport on real estate prices: an empirical analysis of the Dutch housing market. Tinbergen Institute discussion paper, TI 2006-031/3 (2006)

  • Debrezion, G., Pels, E., Rietveld, P.: The impact of railway stations on residential and commercial property value: a meta-analysis. J. Real Estate Finance Econ. 35, 161–180 (2007)

    Article  Google Scholar 

  • Forrest, D., Glen, J., Ward, R.: The impact of a light rail system on the structure of house prices: a hedonic longitudinal study. J. Transp. Econ. Policy 30, 15–29 (1996)

    Google Scholar 

  • F+B: Vergleich von Angebots- und Verkaufspreisen auf dem Hamburger Immobilienmarkt: Studie für die LBS Bausparkasse Hamburg AG. F+B Forschung und Beratung für Wohnen, Immobilien und Umwelt GmbH, Hamburg (2002)

  • Gassdorf, U.: HVV—Warum sind die Preise so hoch? Hamburger Abendblatt, March 15 (2007)

  • Gatzlaff, D.H., Smith, M.T.: The impact of the Miami Metrorail on the value of residences near station locations. Land Econ. 69, 54–66 (1993)

    Article  Google Scholar 

  • Gibbons, S., Machin, S.: Valuing rail access using transport innovations. J. Urban Econ. 57, 148–169 (2005)

    Article  Google Scholar 

  • Grass, G.: The estimation of residential property values around transit station sites in Washington D.C. J. Econ. Finance 16, 139–146 (1992)

    Article  Google Scholar 

  • Green, R.D., James, D.M.: Rail Transit Station Area Development: Small Area Modeling in Washington D.C. M. E. Sharpe, Armonk (1993)

    Google Scholar 

  • Grether, D.M., Mieszkowski, P.: Determinants of real estate values. J. Urban Econ. 1, 127–145 (1974)

    Article  Google Scholar 

  • Halvorsen, R., Palmquist, R.: The interpretation of dummy variables in semilogarithmic equations. Am. Econ. Rev. 70, 474–475 (1980)

    Google Scholar 

  • Halvorsen, R., Pollakowski, H.O.: Choice of functional form for hedonic price equations. J. Urban Econ. 10, 37–49 (1981)

    Article  Google Scholar 

  • Henneberry, J.: Transport investment and house prices. J. Prop. Valuat. Invest. 16, 144–158 (1998)

    Article  Google Scholar 

  • Hochbahn: Annual Report 2008. Hochbahn AG, Hamburg (2009)

    Google Scholar 

  • HVV: Hamburger Verkehrsverbund: Bericht 2008. http://www.hvv.de/pdf/wissenwertes/hvv_bericht_2008.pdf (2009). Accessed 29 March 2010

  • IFEU: UmweltMobilCheck: Wissenschaftlicher Grundlagenbericht. IFEU-Institut für Energie- und Umweltforschung Heidelberg GmbH, Heidelberg (2008)

    Google Scholar 

  • Infas: Mobilität in Deutschland: Ergebnisbericht Hamburg und Umland (Verbundgebiet HVV). Infas Institut für angewandte Sozialwissenschaft GmbH, Bonn (2004)

  • Infas, DIW: Mobilität in Deutschland: Ergebnisbericht. Infas Institut für angewandte Sozialwissenschaft GmbH and Deutsches Institut für Wirtschaftsforschung, Bonn/Berlin (2004)

    Google Scholar 

  • Kennedy, C.A.: A comparison of the sustainability of public and private transportation systems: study of the Greater Toronto Area. Transportation 29, 459–493 (2002)

    Article  Google Scholar 

  • Kim, C.W., Phipps, T.T., Anselin, L.: Measuring the benefits of air quality improvement: a spatial hedonic approach. J. Environ. Econ. Manag. 45, 24–39 (2003)

    Article  Google Scholar 

  • Kim, S.G.: Beeinflussung der Wohnstandortentscheidung für ÖPNV-Lagen durch die Anreizstrategie Location Efficient Value (LEV). Dissertation, Technische Universität Hamburg-Harburg (2003)

  • Knight, J.R.: Listing price, time on market, and ultimate selling price: causes and effects of listing price changes. Real Estate Econ. 30, 213–237 (2002)

    Article  Google Scholar 

  • Leggett, C.G., Bockstael, N.E.: Evidence of the effects of water quality on residential land prices. J. Environ. Econ. Manag. 39, 121–144 (2000)

    Article  Google Scholar 

  • Li, M.M., Brown, H.J.: Micro-neighbourhood externalities and hedonic housing prices. Land Econ. 56, 125–141 (1980)

    Article  Google Scholar 

  • Linneman, P.: Some empirical results on the nature of the hedonic price function for the urban housing market. J. Urban Econ. 8, 47–68 (1980)

    Article  Google Scholar 

  • Löchl, M., Axhausen, K.W.: Modeling hedonic residential rents for land use and transport simulation while considering spatial effects. J. Transp. Land Use 3, 39–63 (2010)

    Google Scholar 

  • McDonald, J.F.: The identification of urban employment subcenters. J. Urban Econ. 21, 242–258 (1987)

    Article  Google Scholar 

  • McMillen, D.P.: Airport expansion and property values: the case of Chicago O’Hare Airport. J. Urban Econ. 55, 627–640 (2004)

    Article  Google Scholar 

  • McMillen, D.P., McDonald, J.F.: Reaction of house prices to a new rapid transit line: Chicago’s Midway Line, 1983–1999. Real Estate Econ. 32, 463–486 (2004)

    Article  Google Scholar 

  • Merlo, A., Ortalo-Magné, F.: Bargaining over residential real estate: evidence from England. J. Urban Econ. 56, 192–216 (2004)

    Article  Google Scholar 

  • Mills, E.S.: Studies in the Structure of the Urban Economy. Johns Hopkins University Press, Baltimore (1972)

    Google Scholar 

  • Muth, R.F.: Cities and Housing. University of Chicago Press, Chicago (1969)

    Google Scholar 

  • Nelson, A.C.: Effects of elevated heavy-rail transit stations on house prices with respect to neighbourhood income. Transp. Res. Rec. 1359, 127–132 (1992)

    Google Scholar 

  • NEORail II: The Effect of Rail Transit on Property Values: A Summary of Studies. NEORail, Cleveland (2001)

    Google Scholar 

  • Poister, T.H.: Transit related crime in suburban areas. J. Urban Aff. 18, 63–75 (1996)

    Article  Google Scholar 

  • Pope, J.C.: Buyer information and the hedonic: the impact of a seller disclosure on the implicit price for airport noise. J. Urban Econ. 63, 498–516 (2008)

    Article  Google Scholar 

  • Redfearn, C.L.: How informative are average effects? Hedonic regression and amenity capitalization in complex urban housing markets. Reg. Sci. Urban Econ. 39, 297–306 (2009)

    Article  Google Scholar 

  • Rosen, S.: Hedonic prices and implicit markets: product differentiation in pure competition. J. Polit. Econ. 82, 34–55 (1974)

    Article  Google Scholar 

  • S-Bahn Hamburg: Die S-Bahn Hamburg in Zahlen. http://www.s-bahn-hamburg.de/s_hamburg/view/wir/daten-zahlen-fakten.shtml (2009). Accessed 29 March 2010

  • Schwanen, T., Mokhtarian, P.L.: What affects commute mode choice: neighborhood physical structure or preferences toward neighborhoods? J. Transp. Geogr. 13, 83–99 (2005)

    Article  Google Scholar 

  • Shin, K., Washington, S., Choi, K.: Effects of transportation accessibility on residential property values: application of spatial hedonic price model in Seoul, South Korea metropolitan area. Transp. Res. Rec. 1994, 66–73 (2007)

    Article  Google Scholar 

  • Simma, A., Axhausen, K.W.: Commitments and modal usage: analysis of German and Dutch panels. Transp. Res. Rec. 1854, 22–31 (2003)

    Article  Google Scholar 

  • Sirpal, R.: Empirical modeling of the relative impacts of various sizes of shopping centers on the values of surrounding residential properties. J. Real Estate Res. 9, 487–505 (1994)

    Google Scholar 

  • Sirmans, G.S., Macpherson, D.A., Zietz, E.N.: The composition of hedonic pricing models. J. Real Estate Lit. 13, 3–43 (2005)

    Google Scholar 

  • Statistikamt Hamburg: Hamburger Stadtteilprofile 2008. Statistisches Amt für Hamburg und Schleswig-Holstein, Hamburg (2009)

    Google Scholar 

  • Strand, J., Vågnes, M.: The relationship between property values and railroad proximity: a study based on hedonic prices and real estate brokers’ appraisals. Transportation 28, 137–156 (2001)

    Article  Google Scholar 

  • Theebe, M.A.J.: Planes, trains and automobiles: the impact of traffic noise on house prices. J. Real Estate Finance Econ. 28, 209–234 (2004)

    Article  Google Scholar 

  • Vessali, K.V.: Land use impacts of rapid transit: a review of the empirical literature. Berkeley Plan. J. 11, 71–105 (1996)

    Google Scholar 

  • Voith, R.: Changing capitalization of CBD-oriented transportation systems: evidence from Philadelphia, 1970–1988. J. Urban Econ. 33, 361–376 (1993)

    Article  Google Scholar 

  • von Thünen, J.H.: Der isolierte Staat in Beziehung auf Landwirtschaft und Nationalökonomie, oder Untersuchungen über den Einfluß, den die Getreidepreise, der Reichthum des Bodens und die Abgaben auf den Ackerbau ausüben. Perthes, Hamburg (1826)

  • Wheaton, W.C.: Urban residential growth under perfect foresight. J. Urban Econ. 12, 1–21 (1982)

    Article  Google Scholar 

  • White, M.J.: Location choice and commuting behavior in cities with decentralized employment. J. Urban Econ. 24, 129–152 (1988)

    Article  Google Scholar 

  • Wilhelmsson, M.: The impact of traffic noise on the values of single-family houses. J. Environ. Plan. Manag. 43, 799–815 (2000)

    Article  Google Scholar 

  • Williams, C. The Pricing of Housing Characteristics in South-East Queensland: An Application of Hedonic Pricing. Working Paper. Queensland University of Technology, Brisbane (1995)

  • Wrigley, M., Wyatt, P. (2001) Transport policy and property values. Paper presented at RICS Cutting Edge Conference, 5–7 September 2001

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Acknowledgments

We would like to thank F+B Forschung und Beratung für Wohnen, Immobilien und Umwelt GmbH, particularly Dr. Bernd Leutner, for providing us with a dataset on condominium prices in the city of Hamburg. We are grateful to two anonymous referees for their helpful comments. We would also like to thank the seminar participants at the University of Hamburg for their comments on earlier versions of this paper.

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  1. Department of Economics, University of Hamburg, Von-Melle-Park 5, 20146, Hamburg, Germany

    Sebastian Brandt & Wolfgang Maennig

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Brandt, S., Maennig, W. The impact of rail access on condominium prices in Hamburg. Transportation 39, 997–1017 (2012). https://doi.org/10.1007/s11116-011-9379-0

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