A Research on Inference from Multiple Distance Variables in Hedonic Regression – Focus on Three Variables
In urban context, urban nodes such as amenity or
hazard will certainly affect house price, while classic hedonic analysis
will employ distance variables measured from each urban nodes.
However, effects from distances to facilities on house prices generally
do not represent the true price of the property. Distance variables
measured on the same surface are suffering a problem called
multicollinearity, which is usually presented as magnitude variance
and mean value in regression, errors caused by instability. In this paper,
we provided a theoretical framework to identify and gather the data
with less bias, and also provided specific sampling method on locating
the sample region to avoid the spatial multicollinerity problem in three
distance variable’s case.
[1] Lancaster, K. J. “A new approach to consumer theory”, Journal of
Political Economy 74, 1966, pp.132–57.
[2] Sherwin Rosen, “Hedonic Prices and Implicit Markets: Product
Differentiation in Pure Competition”, Journal of Political Economy, Vol.
82, No. 1, Jan. - Feb., 1974, pp. 34-55
[3] Silvey, F. “Multicollinearity and Imprecise Estimation”, Journal of the
Royal Statistical Society, Series B, 35, 1985, pp.99-115
[4] Coo, R. D. “Detection of influential observation in linear regression”,
Technometrics,19, 1977, pp.15-18
[5] Herl, Arthur E. and Robert W. Kennard. “Ridge Regression: Biased
Estimation of Nonorthogonal Problems”, Technometrics, 12, 1970a. pp.
55-67.
[6] Hoaglin, D. C., and Welsch, R. E. “The hat matrix in regression and
ANOVA”, Amer. Statistician, 1978, 3217-22. [7] Neter, J, Kun Neter, J., Kutner, M. H., Nachtshein, C. J.,Wasserman,W.,
“Applied Linear Regression Models”,3rd ed. Homewood III, Irwin. 1996.
[8] Danel A. Griffiths, “Modeling Urban Population Density in a
Multi-centered City.” Journal of Urban Economics 9, 1981. pp.298-310.
[9] Eric Heikkila, “Mullticollinearity in Regression Models with Multiple
Distance Measures”, Journal of Regional Science, vol.28. No.3, 1998.
[10] M. Tiefelsdorf, “Misspecifications in interaction model distance
decayrelations: A spatial structure effect” J. Geograph Syst, 2003, 5:
pp25-50
[11] Jaewoo Lee, Keemin Sohn, “Identifying the Impact on Land Prices of
Replacing At-grade or Elevated Railways with Underground Subways in
the Seoul Metropolitan Area”, Urban Studies Journal Limited, 2013,
DOI:10.1177/0042098013484543
[12] Heather A Sander, Debarchana Ghosh, David van Riper, Steven M
Manson, “How do you measure distance in spatial models? An example
using open-space valuation”, Environment and Planning B: Planning
and Design, volume 37, 2010, pp.874 – 894.
[13] Justin M. Ross, Michael C. Farmer, Clifford A. Lipscomb, “Inconsistency
in Welfare Inferences from Distance Variables in Hedonic Regressions”,
Journal of Real Estate Finance and Economics, 43, 2011, pp.385-400.
[14] Hao Huang, Li Yin, “Creating sustainable urban built environments: An
application of hedonic house price models in Wuhan, China”, Springer
Science + Business Media Dordrecht, 2014 J, House and the Built
Environ DOI 10.1007/s 10901-014-9403-8, Urban Geography, Vol. 35,
No.3, 2014, pp. 420-439
[15] D. Schmeidler, “The nucleolus of a characteristic function game”, SIAM
Journal of Applied Mathematics, 17, 1969, pp. 1163–1170
[1] Lancaster, K. J. “A new approach to consumer theory”, Journal of
Political Economy 74, 1966, pp.132–57.
[2] Sherwin Rosen, “Hedonic Prices and Implicit Markets: Product
Differentiation in Pure Competition”, Journal of Political Economy, Vol.
82, No. 1, Jan. - Feb., 1974, pp. 34-55
[3] Silvey, F. “Multicollinearity and Imprecise Estimation”, Journal of the
Royal Statistical Society, Series B, 35, 1985, pp.99-115
[4] Coo, R. D. “Detection of influential observation in linear regression”,
Technometrics,19, 1977, pp.15-18
[5] Herl, Arthur E. and Robert W. Kennard. “Ridge Regression: Biased
Estimation of Nonorthogonal Problems”, Technometrics, 12, 1970a. pp.
55-67.
[6] Hoaglin, D. C., and Welsch, R. E. “The hat matrix in regression and
ANOVA”, Amer. Statistician, 1978, 3217-22. [7] Neter, J, Kun Neter, J., Kutner, M. H., Nachtshein, C. J.,Wasserman,W.,
“Applied Linear Regression Models”,3rd ed. Homewood III, Irwin. 1996.
[8] Danel A. Griffiths, “Modeling Urban Population Density in a
Multi-centered City.” Journal of Urban Economics 9, 1981. pp.298-310.
[9] Eric Heikkila, “Mullticollinearity in Regression Models with Multiple
Distance Measures”, Journal of Regional Science, vol.28. No.3, 1998.
[10] M. Tiefelsdorf, “Misspecifications in interaction model distance
decayrelations: A spatial structure effect” J. Geograph Syst, 2003, 5:
pp25-50
[11] Jaewoo Lee, Keemin Sohn, “Identifying the Impact on Land Prices of
Replacing At-grade or Elevated Railways with Underground Subways in
the Seoul Metropolitan Area”, Urban Studies Journal Limited, 2013,
DOI:10.1177/0042098013484543
[12] Heather A Sander, Debarchana Ghosh, David van Riper, Steven M
Manson, “How do you measure distance in spatial models? An example
using open-space valuation”, Environment and Planning B: Planning
and Design, volume 37, 2010, pp.874 – 894.
[13] Justin M. Ross, Michael C. Farmer, Clifford A. Lipscomb, “Inconsistency
in Welfare Inferences from Distance Variables in Hedonic Regressions”,
Journal of Real Estate Finance and Economics, 43, 2011, pp.385-400.
[14] Hao Huang, Li Yin, “Creating sustainable urban built environments: An
application of hedonic house price models in Wuhan, China”, Springer
Science + Business Media Dordrecht, 2014 J, House and the Built
Environ DOI 10.1007/s 10901-014-9403-8, Urban Geography, Vol. 35,
No.3, 2014, pp. 420-439
[15] D. Schmeidler, “The nucleolus of a characteristic function game”, SIAM
Journal of Applied Mathematics, 17, 1969, pp. 1163–1170
@article{"International Journal of Architectural, Civil and Construction Sciences:69623", author = "Yan Wang and Yasushi Asami and Yukio Sadahiro", title = "A Research on Inference from Multiple Distance Variables in Hedonic Regression – Focus on Three Variables", abstract = "In urban context, urban nodes such as amenity or
hazard will certainly affect house price, while classic hedonic analysis
will employ distance variables measured from each urban nodes.
However, effects from distances to facilities on house prices generally
do not represent the true price of the property. Distance variables
measured on the same surface are suffering a problem called
multicollinearity, which is usually presented as magnitude variance
and mean value in regression, errors caused by instability. In this paper,
we provided a theoretical framework to identify and gather the data
with less bias, and also provided specific sampling method on locating
the sample region to avoid the spatial multicollinerity problem in three
distance variable’s case.
", keywords = "Hedonic regression, urban node, distance variables,
multicollinerity, collinearity.", volume = "9", number = "4", pages = "450-10", }
