Current issue: 56(4)
In this study, logistic regression and neural networks were used to predict non-industrial private forests (NIPF) landowners’ choice of forest taxation basis. The main frame of reference of the study was the Finnish capital taxation reform of 1993. As a consequence of the reform, landowners were required to choose whether to be taxed according to site-productivity or realized-income during the coming transition period of thirteen years.
The most important factor affecting the landowners’ choice of taxation basis was the harvest rate during the transition period, i.e. the chosen timber management strategy. Furthermore, the estimated personal marginal tax rate and the intention to cut timber during next three years affected the choice. The descriptive landowner variables did not have any marked effect on the choice of forest taxation basis.
On average, logistic regression predicted 71% of the choices correctly; the corresponding figure for neural networks was 63%. In both methods, the choice of site-productivity taxation was predicted more accurately than the choice of realized-income taxation. An increase in the number of model variable did not significantly improve the results of neural networks and logistic regression.
The factors affecting the non-industrial, private forest owners’ (NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the forest owners’ choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models.
The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, forest owner’s positive price expectations for the next eight years, forest owner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40.
In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R2 of the optimal regression equation was 0.3 and the relative root mean square error from the test data 0.38.
In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.