Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms

Dubin, Robin A.

doi:10.2307/1926785

Cited by 306 publications

(149 citation statements)

References 13 publications

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“…Regression models with geographically specific dummy variables and distance coefficients have been considered by several authors [14][15][16]. To improve the valuation accuracy other authors, consider spatial information in pricing models using the direct spatial modelling with Cartesian coordinates [17,18], geostatistical models [19], or response surfaces [20][21][22]. Other research [23][24][25][26] has focused on submarkets in which the marginal price contributions of independent variables are more likely to be similar.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mass Appraisal Model Based on Market Segment Parameters

2017

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Abstract:The proposed evaluation scheme is a uni-equation model to evaluate properties of Mass Appraisal (MA) in terms of widespread availability of sample data. It all allows the use of statistical models and in the opposite conditions of the absence of data of comparable properties, the functions of similar market areas are known as well as the ones near to those for which you want to estimate the function. Of course, the accuracy of the evaluation increases with the amount of available data, with other equal conditions and evaluations carried out without data (but in the presence of other market information). It requires extra-statistical appraisal procedures involving a complete knowledge of the real estate market. However, such knowledge is also required in the MA performed by quantitative models with regard to the data sampling and performance monitoring process. The model considers micro-level characteristics of the properties and macro-level parameters of the real estate market segments. The appraisal model defines the prediction function with both the statistical models and estimation procedures. For this purpose, the model considers four specific situations: the construction of a statistical model operating with a sufficiently large sample of market prices; the construction of a prediction function operating with a very few number of market prices samples; in this situation, the appraisal function of market value is defined by using a sample of market prices referred to comparable properties, and these are few for statistical use but perfectly suitable to the appraisal process; the construction of a prediction function operating with only one market price; the construction of a prediction function operating in the absence of real estate data but with similar functions of market areas with other estimated proprieties. The presented model provides a uniform method of estimating the market value of properties (and fees), through the modular functions. The model studied is able to operate also with reduced information, considering the practical circumstances, the boundary conditions, the application precautions and the significance of the results.

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Section: Literature Reviewmentioning

confidence: 99%

A Mass Appraisal Model Based on Market Segment Parameters

2017

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“…Earlier texts include Silk (1979), Cliff and Ord (1981), Miron (1984), Upton and Fingleton (1985), Anselin (1988), and Odland (1988). One of the earliest studies in the real estate field is that of Dubin (1988), who proposed a maximum likelihood method for adjusting for spatial autocorrelation in a hedonic pricing model. Can (1992) then introduced different ways to specify a spatial autoregressive process for housing prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

Wong

Yiu

Chau

2012

J Real Estate Finan Econ

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Spatial dependence is often seen as a problem in econometrics rather than in economics. This study seeks to find an economic explanation for spatially correlated real estate prices. We posit spatial dependence as a process to discover price information from neighboring property transactions. Weaker spatial dependence is expected when price information in the immediate vicinity of a subject property is abundant. In the context of apartment buildings, in addition to the more commonly known horizontal dependence, there is also spatial dependence in the vertical dimension within the same building. Based on more than 18,000 transactions of highly homogeneous apartment units in Hong Kong, we found that the trading volume of a building depresses horizontal spatial dependence, but raises vertical spatial dependence. This not only confirmed the role of trading volume in the real estate price discovery process, but also questioned the validity of constant spatial autocorrelation assumption adopted in many studies.

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“…2000;Anselin & Getis 1992;Can 1990;Anselin 1988, Dubin 1988, Odland 1988. Spatial lag models are used here based on conceptual reasoning.…”

Section: The Hedonic Approachmentioning

confidence: 99%

Valuing the benefits of the urban forest: a spatial hedonic approach

Payton

Lindsey

Wilson

et al. 2008

Journal of Environmental Planning and Management

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ABSTRACT. This paper measures the benefits of the urban forest by examining its effect on housing prices. A Geographic Information System is used to develop a measure of the urban forest, the Normalized Difference Vegetation Index, from satellite imagery and to construct other variables from a variety of sources. Spatial hedonic housing price models for the Indianapolis/Marion County area are estimated. The models indicate that greener vegetation around a property has a positive, significant effect on housing price, holding everything else constant. This effect is dominated by measures at the neighborhood level. These findings indicate that property owners value the urban forest, at least in part, by the premium they pay to live in neighborhoods with greener, denser vegetation. These findings also indicate that public action to maintain and enhance the urban forest may be warranted. Planners and urban foresters can use these findings to inform public and policy debates over urban forestry programs and proposals.

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Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms

Cited by 306 publications

References 13 publications

A Mass Appraisal Model Based on Market Segment Parameters

A Mass Appraisal Model Based on Market Segment Parameters

Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

Valuing the benefits of the urban forest: a spatial hedonic approach

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