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Putting a price on your neighbour

Abstract

Neighbourhood population composition affects the willingness to pay for housing units. This paper utilises a large and rich data set and hedonic regression techniques to disentangle the effect that neighbourhood affluence and presence of inhabitants with an immigrant background have on home prices. Furthermore, we specify an empirical model in a way that also enable us to test for the effect of diversity, both in terms of income levels and of the composition of the immigrant population of a neighbourhood. The hedonic model can be viewed as a variety of an amenity interpretation of the population composition of a neighbourhood. Estimation of effects of population composition is not straightforward as there is good reason to believe that population composition is both endogenously determined together with house prices and that area level omitted variables could bias estimates. This is addressed by lagging the composition measures and by formulating two different models that address these difficulties in different ways. We estimated one random effects model that instruments within neighbourhood variation in population composition and one fixed effects model that control for omitted variables. We find that coefficient estimates are robust across these specifications.

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Notes

  1. 1.

    One important implication of this is that you cannot derive strong conclusions on micro-level attitudes and preferences from observations of macro-level observations.

  2. 2.

    Here we simply use the groupings of country background into 22 categories provided by Statistics Norway. The five largest groups in 2006 was The Nordic group, Other Western Europeans, Other Asians, Pakistanis and Sub-Saharan Africans (not including Somalia).

  3. 3.

    Admittedly after some experimentation with different specification, this yielded inconclusive results. We did, for example, try out a specification where the effects of the concentration measure were allowed to vary with the level of the AA-variable.

  4. 4.

    A large share of the Oslo housing stock consists of co-ops, where each housing units is responsible for its own part of a mutual debt. This mutual debt is added to the sales prices.

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Acknowledgments

This article is based on research funded by the Norwegian Research Council, Grant 217210/H2. We want to thank Arnstein Gjestland at the Stord/Haugesund University College for the work he has done in arranging the zone-related data found in this paper.

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Correspondence to Liv Osland.

Appendix

Appendix

Fixed effects regression of log home prices, fixed effects at the cluster of tracts level.

  Coefficient SE
Log size single family houses 0.588 0.006**
Log size flats 0.644 0.006**
Log size other house types 0.772 0.002**
Construction year 1940, dummy Ref  
Construction year 1940–1950, dummy 0.003 0.005
Construction year 1951–1970, dummy −0.032 0.002**
Construction year 1971–1980, dummy −0.028 0.003**
Construction year 1981–1990, dummy 0.001 0.003
Construction year 1991–2000, dummy 0.049 0.003**
Construction year 2001–2006, dummy 0.127 0.003**
Construction year 2007–, dummy 0.130 0.003**
Owner occupied, dummy Ref  
Co-op, dummy −0.002 0.002
Share in housing company, dummy −0.009 0.003**
Flat ground floor, dummy Ref  
Flat 1st or second floor, dummy 0.027 0.002**
Flat 3rd or 4th floor, dummy 0.052 0.002**
Flat above 4th, dummy 0.083 0.003**
Sold 2009, dummy Ref  
Sold 2010, dummy 0.083 0.002**
Sold 2011, dummy 0.182 0.002**
Sold 2012, dummy 0.251 0.002**
Travel time in minutes from CBD, to the west −0.002 0.0007**
Travel time in minutes from CBD, to the north-east −0.007 0.001**
Travel time in minutes from CBD, to the south −0.0005 0.0004
Log accessibility −0.021 0.006**
Block of flats, dummy Ref  
Single family house, dummy 1.00 0.032**
Row- or terraced house, dummy 0.652 0.031**
Two dwelling houses, dummy 0.662 0.033**
Share public rental housing, neighbourhood 0.112 0.012**
Share single family houses, neighbourhood −0.041 0.007**
Share blocks of flats, neighbourhood −0.024 0.004**
Share small units −0.012 0.009
Share private rental units, neighbourhood 0.073 0.009**
Share AA, neighbourhood 0.211 0.011**
Share largest minority, neighbourhood 0.039 0.005**
Log median male income, neighbourhood 0.130 0.009**
SD median male income, neighbourhood 0.0002 0.00001**
Indicator truncated SD, neighbourhood dummy −0.002 0.003
Intercept 10.814 0.096
R2-within 0.774  
R2-between 0.674  
R2-overall 0.740  
Rho 0.483  
N 98,568  
  1. Note that neighbourhood in the table above refer to census tracts
  2. SE denotes clustered standard errors
  3. Significance levels of 0.01 and 0.05 is marked with ** and *

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Nordvik, V., Osland, L. Putting a price on your neighbour. J Hous and the Built Environ 32, 157–175 (2017). https://doi.org/10.1007/s10901-016-9506-5

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Keywords

  • Home prices
  • Neighbourhood characteristics
  • Diversity
  • Urban sorting
  • Oslo