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Yayın Reviewing the effects of spatial features on price prediction for real estate market: Istanbul case(IEEE, 2022-09-16) Ecevit, Mert İlhan; Erdem, Zeki; Dağ, HasanIn the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, which is commonly used for price prediction. This study reviews spatial features' effects on predicting the house price ranges for real estate in Istanbul, Turkey, in the classification context. Spatial features are generated and extracted by geocoding the address information from the original data set. This geocoding and feature extraction is another challenge in this research. The experiments compare the performance of Decision Trees (DT), Random Forests (RF), and Logistic Regression (LR) classifier models on the data set with and without spatial features. The prediction models are evaluated based on classification metrics such as accuracy, precision, recall, and F1-Score. We additionally examine the ROC curve of each classifier. The test results show that the RF model outperforms the DT and LR models. It is observed that spatial features, when incorporated with non-spatial features, significantly improve the prediction performance of the models for the house price ranges. It is considered that the results can contribute to making decisions more accurately for the appraisal in the real estate industry.Yayın A parallel implementation: Real space Green's function technique(Sage Publications, 2007-04) Onat, Berk; Durukanoğlu, Sondan; Dağ, HasanWe develop an MPI-based parallel algorithm to implement the real space Green's function technique for calculating the vibrational density of states corresponding to a solid. The Hamiltonian describing the interactions between the atoms within the system is obtained from the embedded atom method. The parallel implementation speeds up calculation by an order of magnitude. The parallel implementation details and results are presented in this paper.Yayın Comparison of evolutionary techniques for Value-at-Risk calculation(Springer-Verlag Berlin, 2007) Uludağ, Gönül; Etaner Uyar, Ayşe Şima; Senel, Kerem; Dağ, HasanThe Value-at-Risk (VaR) approach has been used for measuring and controlling the market risks in financial institutions. Studies show that the t-distribution is more suited to representing the financial asset returns in VaR calculations than the commonly used normal distribution. The frequency of extremely positive or extremely negative financial asset returns is higher than that is suggested by normal distribution. Such a leptokurtic distribution can better be approximated by a t-distribution. The aim of this study is to asses the performance of a real coded Genetic Algorithm (CA) with Evolutionary Strategies (ES) approach for Maximum Likelihood (ML) parameter estimation. Using Monte Carlo (MC) simulations, we compare the test results of VaR simulations using the t-distribution, whose optimal parameters are generated by the Evolutionary Algorithms (EAs), to that of the normal distribution. It turns out that the VaR figures calculated with the assumption of normal distribution significantly understate the VaR figures computed from the actual historical distribution at high confidence levels. On the other hand, for the same confidence levels, the VaR figures calculated with the assumption of t-distribution are very close to the results found using the actual historical distribution. Finally, in order to speed up the MC simulation technique, which is not commonly preferred in financial applications due to its time consuming algorithm, we implement a parallel version of it.
