The effect of educational test scores on house prices in a model with spatial dependence

Sedgley, Norman; Williams, Nancy A.; Derrick, Frederick W.

doi:10.1016/j.jhe.2007.12.003

Cited by 39 publications

(26 citation statements)

References 30 publications

(30 reference statements)

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“…4 This method was used in some empirical analyses, including Kim et al (2003) and Sedgley et al (2008).…”

Section: Spatial Two-stage Least Squares Methodsmentioning

confidence: 99%

Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances

Yokoi

2010

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The likelihood functions for spatial autoregressive models with normal but heteroskedastic disturbances have been derived [Anselin (1988, ch.6)], but there is no implementation of maximum likelihood estimation for these likelihood functions in general cases with heteroskedastic disturbances. This is the reason why less efficient IV-based methods, 'robust 2-SLS' estimation for example, must be applied if disturbance terms might be heteroskedastic. In this paper, we develop a new computer program for maximum likelihood estimation and confirm the efficiency of our estimator in cases with heteroskedastic disturbances, using Monte Carlo simulations. Terms of use: Documents in EconStor may

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“…4 This method was used in some empirical analyses, including Kim et al (2003) and Sedgley et al (2008).…”

Section: Spatial Two-stage Least Squares Methodsmentioning

confidence: 99%

Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances

Yokoi

2010

SSRN Journal

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“…Some (Cohen and Coughlin, 2008) revealed the relationship between airport noise level and real estate value. Some (Sedgley, Williams et al, 2008) detected how public school quality influence real estate markets in Howard County, Maryland. Some (Visser, Frank et al, 2008) used Hedonic price modelling to derive different models of property price from which the contribution of the characteristics of the residential environment were estimated.…”

Section: Introductionmentioning

confidence: 99%

Using GIS and Hedonic in the modelling of spatial variation of housing price in Xiamen city

et al. 2013

IRSPSD International

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Abstract:Valuing the built environment with the support of GIS and housing price model is an interesting topic because it connects the spatial thinking with economic concerns. In this paper, we chose the ordinary commercial housing in Xiamen City as our research targets, and the technology of GIS and Hedonic price model were used. Xiamen city is a typical coastal city, and from the research result, we found that the distribution of higher housing price was kept consistent with the coastal areas of Xiamen city. Besides, the average housing price of Xiamen inner-island (the old urban island area) was far higher than the outer-island. To reveal the degree of different spatial elements affecting housing price, we chose 21 variables (three types: location characteristic, structure characteristic and neighbourhood characteristic) and measured them in GIS platform. The Hedonic model showed that the main elements that influencing the price of commodity housings in Xiamen included Property Management Fee, Distance to Commercial Center, Distance to Hospital, Distance to Primary School, Near Village or Not, Near Green space or Not, Near kindergarten or Not. At the end of the paper, we provided three suggestions to balance the spatial variation of housing price in Xiamen City.

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“…The nearest neighbors are the jurisdictions who are closest in terms of linear distance. The optimal number of nearest neighbors is fairly arbitrary, but is typically between five and twenty (Sedgley et al, 2008). We separately run the specification using 1-10 neighbors as the nearest neighbor designation and conduct Bayesian analysis to determine the nearest neighbor specification which bests describes the data.…”

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confidence: 99%

Spatial Spillovers of Land Use Regulation in the United States

Christafore

Leguizamon

2015

Housing Studies

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Evidence of spatial dependence in land use regulatory levels was first found in Brueckner (1998) for California cities. Recent research has not incorporated this consideration despite the considerable consequences of the relationship. We seek to expand the empirical findings to a current, larger and more diverse data set for municipalities across the United States. Analyzing regulatory levels and their determinants from over 2,000 municipalities and 300 Metropolitan Statistical Areas, we find strong evidence of spatial dependence at the local level and aggregated metropolitan level. This suggests that political competition, rather than welfare maximization exclusively, may be influencing the level of regulations adopted.JEL Classification: H23, H73, R28, R38

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The effect of educational test scores on house prices in a model with spatial dependence

Cited by 39 publications

References 30 publications

Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances

Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances

Using GIS and Hedonic in the modelling of spatial variation of housing price in Xiamen city

Spatial Spillovers of Land Use Regulation in the United States

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