||Beside this valuation methodology stipulated by the International Valuation Standards Committee, the Automated Valuation Models (AVM) are growing in acceptance. These rely on statistical models such as the multiple regression analysis or geographic information systems (GIS). While AVMs can be quite accurate, particularly when used in a very homogenous area, there is also evidence that AVMs are not accurate in other instances such as when they are used in rural areas, or when the appraised property does not conform well to the neighborhood. AVMs have also gained favor in class action litigation and have been substantiated in numerous cases as appropriate method for dealing with large-scale real estate litigation and retrocession problems. The central idea of this Paper is that instead of teaching based around three approaches to value we should base teaching on concepts of price distributions, pricing models and prediction error analysis. This ground real estate valuation more firmly in modern economics and finance theory and statistical methods as they have developed in recent academic literature. The key concepts are: possible price distribution, pricing model and error analysis. Thus, the price = coefficients * characteristics + error. Rely on these elements, the valuation methods fall into two main categories: objective and subjective. The objective methods stipulate the valuation as a science, leads to setting out methods and standards and performance criteria. The valuer reasons from evidence using quantitative methods. The subjective methods confirm the valuation as art. The valuer creates a value by subjective opinion based on experience. Modern societies are dedicated to rationality rather than superstition. The use of econometrics and regression analysis is a superior tool compared to so-called “traditional” appraisal techniques. Appraisal valuation modeling techniques augment traditional appraisal practice, which differentiates them from automated valuation model systems that literally replaces the appraiser.