As far as the heteroscedasticity is concerned, it will cause the estimated variance of the coefficient to influence both direction, upward and downward. In other words, the estimated of variance of the coefficient can be overestimated or estimated depends on the nature of causing factors in the model. If we persist in using the usual testing procedures despite heteroscedasticity, whatever conclusions we draw or inferences we make be very misleading. As a result, we can no longer rely on the conventionally computed confidence intervals and the conventionally employed t and F tests. The most important conclusions of this paper in terms of methodological point of view can be summarized as follows: (i) Hedonic models of housing prices must be corrected for heteroscedasticity to ensure greater efficiency in the estimation of hedonic prices wherever the heteroscedasticity does exist, (ii) After careful detection of heteroscedasticity, the method of WLS(Weighted Least Squares) can provide more efficient estimate of the hedonic housing prices in the study area than the traditional OLS(Ordinary Least Squares), (iii) Spatial econometric model, such as SEM(Spatial Errors Model), can be more efficient estimator than the WLS method, and (iv) The spatial econometric model, SEM, is preferable as well as convenient to employ the application.
I. Introduction
II. Data Description and Research Area
III. The Problem of Heteroscedasticity
IV. Detection of Heteroscedasticity and Spatial Autocorrelation
V. The Comparison of the Models
V. Conclusions
References
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