References
- Aitkin, M.; Anderson, D.; Hinde, J. 1981. Statistical modelling of data on teaching styles, Journal of the Royal Statistical Society, Series A 144: 148–161. http://dx.doi.org/10.2307/2981826
- Antipov, E. A.; Pokryshevskaya, E. B. 2012. Mass appraisal of residential apartments: an application of Random forest for valuation and CART-based approach for model diagnostics, Expert Systems with Applications 39: 1772–1778. http://dx.doi.org/10.1016/j.eswa.2011.08.077
- Aznar, J.; Ferrís-Oñate, J.; Guijarro, F. 2010. An ANP framework for property pricing combining quantitative and qualitative attributes, Journal of the Operational Research Society 61(5): 740–755. http://dx.doi.org/10.1057/jors.2009.31
- Aznar, J.; Guijarro, F.; Moreno-Jiménez, J. M. 2011. Mixed valuation methods: a combined AHP-GP procedure for individual and group multicriteria agricultural valuation, Annals of Operations Research 190(1): 221–238. http://dx.doi.org/10.1007/s10479-009-0527-2
- Basu, S.; Thibodeau, T. G. 1998. Analysis of spatial autocorrelation in house prices, Journal of Real Estate Finance and Economics 17(1): 61–85. http://dx.doi.org/10.1023/A:1007703229507
- Brown, K. H.; Uyar, B. 2004. A hierarchical linear model approach for assessing the effects of house and neighborhood characteristics on housing prices, Journal of Real Estate Practice and Education 7(1): 15–23.
- Cervelló, R.; García, F.; Guijarro, F. 2011. Ranking residential properties by a multicriteria single price model, Journal of the Operational Research Society 62: 1941–1950. http://dx.doi.org/10.1057/jors.2010.170
- D’Amato, M. 2007. Comparing rough set theory with multiple regression analysis as automated valuation methodologies, International Real Estate Review 10(2): 42–65.
- D’Amato, M. 2010. A location value response surface model for mass appraising: an “iterative” location adjustment factor in Bari, Italy, International Journal of Strategic Property Management 14(3): 231–244. http://dx.doi.org/10.3846/ijspm.2010.17
- Downes, T. A.; Zabel, J. E. 2002. The impact of school characteristics on house prices: Chicago, Journal of Urban Economics 52: 1–25. http://dx.doi.org/10.1016/S0094-1190(02)00010-4
- Duncan, C.; Jones, K.; Moon, G. 1993. Do places matter? A multilevel analysis of regional variations in healthrelated behaviour in Britain, Social science and Medicine 37: 725–733. http://dx.doi.org/10.1016/0277-9536(93)90366-C
- Ecer, F. 2014. A hybrid banking websites quality evaluation model using AHP and COPRAS-G: a Turkey case, Technological and Economic Development of Economy 20(4): 758–782. http://dx.doi.org/10.3846/20294913.2014.915596
- Fagan, A. A.; Wright, E. M.; Pinchevsky, G. M. 2015. Exposure to violence, substance use, and neighborhood context, Social Science Research 49: 314–326. http://dx.doi.org/10.1016/j.ssresearch.2014.08.015
- Fan, G. Z.; Ong, S. E.; Koh, H. C. 2006. Determinants of house price: a decision tree approach, Urban Studies 43(12): 2301–2315. http://dx.doi.org/10.1080/00420980600990928
- Farmer, M. C.; Lipscomb, C. A. 2010. using quantile regression in hedonic analysis to reveal submarket competition, Journal of Real Estate Research 32(4): 435–460.
- Ferreira, E.; Sirmans, G. 1988. Ridge regression in real estate analysis, The Appraisal Journal 56(3): 311–319.
- García, N.; Gámes, M.; Alfaro, E. 2008. Ann + GIS: an automated system for property valuation, Neurocomputing 71: 733–742. http://dx.doi.org/10.1016/j.neucom.2007.07.031
- Gelman, A.; Fagana, J.; Kiss, A. 2007. An analysis of the new york City Police Department’s “stop-and-frisk” policy in the context of claims of racial bias, Journal of the American Statistical Association 102(479): 813–823. http://dx.doi.org/10.1198/016214506000001040
- Giuliano, G.; Gordon, P.; Pan, Q.; Park, J. Y. 2010. Accessibility and residential land values: Some tests with new measures, Urban Studies 47(14): 3103–3130. http://dx.doi.org/10.1177/0042098009359949
- Gloudemans, R. J. 1999. Mass appraisal of real property. International Association of Assessing Officers.
- Isakson, H. R. 2001. using multiple regression in real estate appraisal, The Appraisal Journal 69(4): 424–430.
- Kontrimas, V.; Verikas, A. 2011. The mass appraisal of real estate by computational intelligence, Applied Soft Computing 11: 443–448. http://dx.doi.org/10.1016/j.asoc.2009.12.003
- Lee, C. C. 2009. Hierarchical linear modelling to explore the influence of satisfaction with public facilities on housing prices, International Real Estate Review 12(3): 252–272.
- Mihi-Ramirez, A.; Metelski, D.; Rudžionis, A. 2013. The migration flow between Lithuania and Spain: a study of economic factors, Intellectual Economics 7(4): 426–438. http://dx.doi.org/10.13165/IE-13-7-4-02
- Narula, S. C.; Wellington, J. F.; Lewis, S. A. 2012. Valuating residential real estate using parametric programming, European Journal of Operational Research 217: 120–128. http://dx.doi.org/10.1016/j.ejor.2011.08.014
- Palmquist, R. B. 1984. Estimating the demand for the characteristics of housing, Review of Economics and Statistics 66: 394–404. http://dx.doi.org/10.2307/1924995
- R Core Team. 2014. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.
- Raudenbush, S. W.; Bryk, A. S. 1986. A hierarchical model for studying school effects, Sociology of Education 59(1): 1–17. http://dx.doi.org/10.2307/2112482
- Rudzkis, R.; Valkavičienė, R. 2014. Econometric models of the impact of macroeconomic processes on the stock market in the Baltic countries, Technological and Economic Development of Economy 20(4): 783–800. http://dx.doi.org/10.3846/20294913.2014.949901
- Štreimikienė, D. 2014. Housing indicators for assessing quality of life in Lithuania, Intellectual Economics 8(1): 25–41. http://dx.doi.org/10.13165/IE-14-8-1-02
- Titko, J.; Stankevičienė, J.; Lāce, N. 2014. Measuring bank efficiency: DEA application, Technological and Economic Development of Economy 20(4): 739–757. http://dx.doi.org/10.3846/20294913.2014.984255
- Raslanas, S.; Zavadskas, E. K.; Kaklauskas, A.; Zabulenas, A. R. 2010. Land value tax in the context of sustainable urban development and assessment. Part II – analysis of land valuation techniques: the case of Vilnius, International Journal of Strategic Property Management 14(2): 173–190. http://dx.doi.org/10.3846/ijspm.2010.13
- Selim, H. 2009. Determinants of house prices in Turkey: hedonic regression versus artificial neural network, Expert Systems with Applications 36(2): 2843–2852. http://dx.doi.org/10.1016/j.eswa.2008.01.044
- Singh, J. 2014. Effect of school and home factors on learning outcomes at elementary school level: a hierarchical linear model, Education 3-13 44(2): 116–139. http://dx.doi.org/10.1080/03004279.2014.899383
- Snijders, T.; Bosker, R. 1999. Multilevel analysis: an introduction to basic and advanced multilevel modelling. London: Sage Publications.
- Tanaka, H.; Uejima, S.; Asai, K. 1982. Linear regression analysis with fuzzy model, IEEE Transactions on Systems Man and Cybernetics 12(6): 903–907. http://dx.doi.org/10.1109/TSMC.1982.4308925
- Tay, D.; Ho, D. 1992. Artificial intelligence and the mass appraisal of residential apartments, Journal of Property Valuation and Investment 10(2): 525–540. http://dx.doi.org/10.1108/14635789210031181
- Tso, G. K.; Guan, J. 2014. A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption, Energy 66: 722–731. http://dx.doi.org/10.1016/j.energy.2014.01.056
- Wang, W.; Rothschild, D.; Goel, S.; Gelman, A. 2015. Forecasting elections with non-representative polls, International Journal of Forecasting 31(3): 980–991. http://dx.doi.org/10.1016/j.ijforecast.2014.06.001