Developing an automated valuation model for estimating land values in Limassol
This study employs the statistical method of Multiple Linear Regression analysis (MLR) to develop an Automated Valuation Model (AVM) for estimating land values by utilizing transaction-based data in Limassol, Cyprus. The authors focus on the confidence level and accuracy of the value estimated by an AVM. Thus, the developed AVM was tested in two contrasting areas of Limassol in terms of location characteristics and market conditions. Most AVMs contain a statistical method to generate the estimated value of a real estate property. However, the outcome of a statistical method is verified by statistical measures. Therefore, if the validation of the predicted value for its accuracy derives from the statistical metrics of the model, then the explanatory variables cannot remain constant. It is implied that the AVM in order to grant the highest statistical metrics for a given property valuation requires different combination of independent variables in different locations, which means that the parameters of the model should change or adjust for every case to obtain the best fit model. The authors demonstrate that the best fit model is obtained when several models are executed with alternative combinations of variables. Hence, the best fit to the regression is given by the model with the better statistical measures when compared to the other models. Consequently, the predicted value is supported by statistical significance and can be adopted at a high confidence level.
For the full article follow the link below.