ABSTRACT
With the advent of large computerized databases, computational techniques are being relied on more frequently to estimate residential property values. As an alternative to the most commonly used computational technique of multiple regression, this application describes how a neural network was applied to estimate the selling price of single-family residential properties in one area of a large California city. For the holdout sample of 100 properties, the average absolute difference between the actual selling price and the estimated selling price generated by the neural network was 9.48%. In terms of comparative accuracy, the network was able to achieve, on average, more accurate valuations of properties than the multiple regression model in the holdout sample. The network also produced more accurate valuations than the multiple regression model for 57 out of the 100 residential properties in the holdout sample.
