The Modelling of Residential Sales Prices with Kriging Using Different Distance Metrics in Different Correlation Functions

Modelling and estimating sales prices based on economical conditions is so important for housing sector. In this study, firstly; investigated economic variables effected mostly to housing sales prices and created kriging model for housing sales prices in Dubai. For effective kriging model, researchers need to select most effective correlation functions and optimum correlation parameters. To determine better correlation function structure, used Euclidian and Canberra distances for both Exponential and Gaussian correlation functions. Simulation studies were applied to obtain optimum correlation function parameters based on Maximum Likelihood Estimation(MLE)  procedure. For detecting normality of reponse values, Focused Information Criteria(FIC) was used. Based on Cross Validation criteria, selected best correlation function with best distance metrics.

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  • D. R. Jones, M. Schonlau, and W. J. Welch, Efficient Global Optimization of Expensive Black-Box Functions, Journal of Global Optimization, 13, 455–492, (1998),
  • M. SchonlauComputer Experiments and Global Optimization,Phd. Thesis, University of Waterloo, (1997).
  • R. Kelley Pace, Ronald Barry, C. F. Sirmans, Spatial Statistics and Real Estate, The Journal of Real Estate Finance and Economics, Volume 17, Issue 1, pp 5-13, (1998).
  • Robin Dubin, Kelley Pace, And Thomas Thibodeau , Spatial Autoregression Techniques for Real Estate Data, Journal of Real Estate Literature, Vol. 7, No. 1, pp. 79-95, (1999).
  • Jorge Chica-Olmo, Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging. Journal of Real Estate Research, Vol. 29, No. 1, pp. 91-114, (2007).
  • Zuo Zhang, Jiangfeng Li, Application of GIS and Spatial Decision Support System for Affordable Housing, Proceedings of 2009 4th International Conference on Computer Science & Education, (2009).
  • Frank C. Curriero, On the Use of Non-Euclidean Distance Measures in Geostatistics, Mathematical Geology, Volume 38, Issue 8, pp 907-926, (2006).
  • Moti L. Tiku and Aysen D. Dikkaya, Robust Estimation and Hypothesis Testing, New Age International (P) Limited, Publishers, New Delhi, pp 22-23, (2004).
  • Archana Singh, Avantika Yadav, Ajay Rana (2013), K-means with Three different Distance Metrics, International Journal of Computer Applications, 67(10), pp 13-17.
  • Schulz Jan., "Canberra distance". In online. http://www.code10.info/index.php?option=com_content&view=article&id=49:article_canberra-distance&catid=38:cat_coding_algorithms_data-similarity&Itemid=57.
  • REIDIN, Real Estate Information Services, www.reidin.com.