Specifying and Estimating Multi-Level Models for Geographical Research

It is argued that multi-level models based on shrinkage estimators represent a considerable improvement over single-level models estimated by ordinary-least squares. In substantive terms, the ML models allow relationships to vary in time and space according to context. Shrinkage estimators make very efficient use of the information contained in the hierprchical data sets that are estimated by ML models. A number of ML models for house-price variation are specified in terms of fixed and random, allowed-to-vary, effects. Empirical illustrations of some of these ML models are given for house-price variation in Southampton.