Spatial autocorrelation and neighborhood quality

Although it is intuitively obvious that neighborhood quality and accessibility should affect housing prices, the empirical evidence is weak: Most hedonic estimations show few significant coefficients on the neighborhood and accessibility variables. The lack of empirical support for the capitalizatio...

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Published inRegional science and urban economics Vol. 22; no. 3; pp. 433 - 452
Main Author Dubin, Robin A.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.09.1992
Elsevier
North-Holland
Elsevier Sequoia S.A
SeriesRegional Science and Urban Economics
Subjects
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Abstract Although it is intuitively obvious that neighborhood quality and accessibility should affect housing prices, the empirical evidence is weak: Most hedonic estimations show few significant coefficients on the neighborhood and accessibility variables. The lack of empirical support for the capitalization of neighborhood and accessibility effects may stem from the multicentric nature of the city as well as measurement problems with regard to neighborhood quality. In this paper, an alternative approach is taken: Omit all neighborhood and accessibility measures from the set of explanatory variables and instead model the resulting autocorrelation in the error term. Data from Baltimore are used in an empirical example; the results show that this approach provides a very plausible pattern of housing price variation.
AbstractList Although it is intuitively obvious that neighborhood quality and accessibility should affect housing prices, the empirical evidence is weak: Most hedonic estimations show few significant coefficients on the neighborhood and accessibility variables. The lack of empirical support for the capitalization of neighborhood and accessibility effects may stem from the multicentric nature of the city as well as measurement problems with regard to neighborhood quality. In this paper, an alternative approach is taken: Omit all neighborhood and accessibility measures from the set of explanatory variables and instead model the resulting autocorrelation in the error term. Data from Baltimore are used in an empirical example; the results show that this approach provides a very plausible pattern of housing price variation.
Although it is intuitively obvious that neighborhood quality and accessibility should affect housing prices, the empirical evidence is weak. Most hedonic estimations show few significant coefficients on the neighborhood and accessibility variables. The lack of empirical support for the capitalization of neighborhood and accessibility effects may stem from the multicentric nature of the city as well as measurement problems with regard to neighborhood quality. In an alternative approach, all neighborhood and accessibility measures are omitted from the set of explanatory variables. Instead, the resulting autocorrelation in the error term is modeled. Data from Baltimore, Maryland, are used in an empirical example. In the analysis of the Baltimore housing market, sizable neighborhood effects are revealed. Both premium and penalties occur in plausible locations. The results show that the approach provides a very plausible pattern of housing price variation.
Although it is intuitively obvious that neighborhood quality & accessibility should affect housing prices, the empirical evidence is weak: most hedonic estimations show few significant coefficients on the neighborhood & accessibility variables. It is suggested that the lack of empirical support for the capitalization of neighborhood & accessibility effects may stem from the multicentric nature of the city as well as measurement problems with regard to neighborhood quality. Here, an alternative approach is taken that omits all neighborhood & accessibility measures from the set of explanatory variables & instead models the resulting autocorrelation in the error term. Data on 1,493 house sales in Baltimore, Md, are used in an empirical test, with results indicating that this approach provides a very plausible pattern of housing price variation. 4 Tables, 4 Figures, 20 References. Adapted from the source document.
Author Dubin, Robin A.
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Dubin, Sung (BIB9) 1990; 27
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Kain (10.1016/0166-0462(92)90038-3_BIB13) 1970; 65
Ball (10.1016/0166-0462(92)90038-3_BIB1) 1973; 10
Jackson (10.1016/0166-0462(92)90038-3_BIB11) 1979; 6
Brigham (10.1016/0166-0462(92)90038-3_BIB3) 1965; 41
Can (10.1016/0166-0462(92)90038-3_BIB4) 1990; 66
Grieson (10.1016/0166-0462(92)90038-3_BIB10) 1989; 25
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Maser (10.1016/0166-0462(92)90038-3_BIB15) 1977; 20
Richardson (10.1016/0166-0462(92)90038-3_BIB18) 1988; 22
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Matheron (10.1016/0166-0462(92)90038-3_BIB16) 1963; 58
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Dubin (10.1016/0166-0462(92)90038-3_BIB9) 1990; 27
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Snippet Although it is intuitively obvious that neighborhood quality and accessibility should affect housing prices, the empirical evidence is weak: Most hedonic...
Although it is intuitively obvious that neighborhood quality and accessibility should affect housing prices, the empirical evidence is weak. Most hedonic...
Although it is intuitively obvious that neighborhood quality & accessibility should affect housing prices, the empirical evidence is weak: most hedonic...
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StartPage 433
SubjectTerms Baltimore, Maryland
Economic models
Economic Theory
Housing
Housing Costs
Housing market
Hypotheses
Neighborhoods
Neighbourhoods
Prices
Property values
Quality of Life
Real estate appraisal
Urban planning
Title Spatial autocorrelation and neighborhood quality
URI https://dx.doi.org/10.1016/0166-0462(92)90038-3
http://econpapers.repec.org/article/eeeregeco/v_3a22_3ay_3a1992_3ai_3a3_3ap_3a433-452.htm
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