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Spatial Autoregression Techniques for Real Estate Data

Spatial Autoregression Techniques for Real Estate Data This paper describes how spatial techniques can be used to improve the accuracy of market value estimates obtained using multiple regression analysis. Rather than eliminating the problem of spatial residual dependencies through the inclusion of many independent variables, spatial statistical methods typically keep fewer independent variables and augment these with a simple model of the spatial error dependence. We discuss alternative spatial autoregression model specifications, estimation methods, and prediction procedures. An empirical example is provided in the appendix. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Real Estate Literature Taylor & Francis

Spatial Autoregression Techniques for Real Estate Data

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Spatial Autoregression Techniques for Real Estate Data

Abstract

This paper describes how spatial techniques can be used to improve the accuracy of market value estimates obtained using multiple regression analysis. Rather than eliminating the problem of spatial residual dependencies through the inclusion of many independent variables, spatial statistical methods typically keep fewer independent variables and augment these with a simple model of the spatial error dependence. We discuss alternative spatial autoregression model specifications, estimation...
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Publisher
Taylor & Francis
Copyright
© 1999 American Real Estate Society
ISSN
1573-8809
DOI
10.1080/10835547.1999.12090079
Publisher site
See Article on Publisher Site

Abstract

This paper describes how spatial techniques can be used to improve the accuracy of market value estimates obtained using multiple regression analysis. Rather than eliminating the problem of spatial residual dependencies through the inclusion of many independent variables, spatial statistical methods typically keep fewer independent variables and augment these with a simple model of the spatial error dependence. We discuss alternative spatial autoregression model specifications, estimation methods, and prediction procedures. An empirical example is provided in the appendix.

Journal

Journal of Real Estate Literature – Taylor & Francis

Published: Jan 1, 1999

References

  • Kriging with Large Data Sets Using Sparse Matrix Techniques
  • Analysis of Spatial Autocorrelation in House Prices
  • Mass Appraisals via Multiple Regression Analysis
  • Specification and Estimation of Hedonic Housing Price Models
  • Spatial Dependence and House Price Index Construction
  • How GIS Can Put Urban Economic Analysis on the Map
  • Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms
  • Spatial Autocorrelation and Neighborhood Quality
  • Predicting House Prices Using Multiple Listings Data
  • Mass Versus Individual Appraisals
  • Best Linear Unbiased Prediction in the Generalized Linear Regression Model
  • A Generalized Spatial Two-Stage Least-Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances
  • Quick Computation of Regressions with a Spatially Autoregressive Dependent Variable
  • Fast CARs
  • Using the Spatial Configuration of the Data to Improve Estimation
  • Generalizing the OLS and the Grid Estimator