Models for Spatially Dependent Missing Data

Models for Spatially Dependent Missing Data Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known characteristics. In fact, explanatory variable information on house characteristics for all properties, sold and unsold, are often available from assessors. We set forth an estimation approach that predicts missing values of the dependent variable when the sample data exhibit spatial dependence. Employing information on the housing characteristics of both sold and unsold properties can improve prediction, increase estimation efficiency for the missing-at-random case, and reduce self-selection bias in the non-missing-at-random case. We demonstrate these advantages with a Monte Carlo experiment as well as with actual housing data. The Journal of Real Estate Finance and Economics Springer Journals

Models for Spatially Dependent Missing Data

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Kluwer Academic Publishers
Copyright © 2004 by Kluwer Academic Publishers
Economics; Regional/Spatial Science; Financial Services
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