English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

. The purpose of this article is to examine the predictionaccuracy of the Random Forests, a machine learning method, when it isapplied for residential mass appraisals in the city of Nicosia, Cyprus. Theanalysis is performed using transaction sales data from the CyprusDepartment of Lands and Surveys, the Consumer Price Index of Cyprus from theCyprus Statistical Service and the Central Bank of Cyprus' Residential Index(Price index for apartments). The Consumer Price Index and the price indexfor apartments record quarterly price changes, while the dependent variablesfor the computational models were the Declared and the Accepted Prices thatwere conditional on observed values of a variety of independent variables.The Random Forests method exhibited enhanced prediction accuracy, especiallyfor the models that comprised of a sufficient number of independentvariables, indicating the method as prominent, although it has not yet beenutilized adequately for mass appraisals.

advances in geosciences

Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus