Wong Mei Chin
ABSTRACT
The real estate sector has been a key factor in development of economy in every country. The valuation of property is also vital for the real estate industry. Thus, this paper presents the best fitting model in conducting the valuation of residential real estate. The main objective of this work was to understand the standard valuation procedures by multiple regression approaches. The type of properties that are used in this study is condominiums that are located within Kuala Lumpur. Multiple regression approach is used in conducting the valuation for these condominiums selected as sample. The data were analyzed using SPSS software based on the dependent variable (asking price) and six independent variable, which were number of bedrooms, number of bathrooms, number of stories, square feet, distance towards rail station and distance towards KLCC. Results of this work showed that multiple regression approach can be utilized to model the value of residential real estate and key findings showed that that all of the independent variables contribute to the model except the distance towards rail station. Our analysis has indicated that the distance towards rail station does not affect the price of condominium in Kuala Lumpur.
- Dawidowicz, A., Radzewicz, A., and Renigier-Biłozor, M., Algorithm for purposes of determining real estate markets efficiency with help of land administration system. Survey review, 46(336), 189--204, 2014.Google Scholar
- Cupal, M., The Comparative Approach theory for real estate valuation. Procedia-Social and Behavioral Sciences, 109, 19--23, 2014.Google ScholarCross Ref
- Cellmer, R. Spatial analysis of the effect of noise on the prices and value of residential real estates. Geomatics and Environmental Engineering, 5(4), 13--28, 2011.Google Scholar
- Adamczyk, T., and Bieda, A., Intended use of real estate as a time changeable attribute for determining compensation for nationalized and expropriated lands. Real Estate Management and Valuation, 22(4), 35--50, 2014.Google ScholarCross Ref
- Selvachandran, G., and Salleh, A. R., Possibility vague soft expert set theory and its application in decision making. In International Conference on Soft Computing in Data Science, Springer, Singapore, pp. 77--87, 2015.Google ScholarCross Ref
- Selvachandran, G., Maji, P. K., Faisal, R. Q., and Salleh, A. R., Distance and distance induced intuitionistic entropy of generalized intuitionistic fuzzy soft sets. Applied Intelligence, 47(1), 132--147, 2017. Google ScholarDigital Library
- Selvachandran, G., and John, S. J. Possibility interval-valued vague soft expert sets and its similarity measure. International Journal of Fuzzy System Applications (IJFSA), 6(1), 108--121, 2017. Google ScholarDigital Library
- Selvachandran, G., and Peng, X., A modified TOPSIS method based on vague parameterized vague soft sets and its application to supplier selection problems. Neural Computing and Applications, 1--16, 2018.Google Scholar
- Selvachandran, G., Quek, S., Smarandache, F., and Broumi, S,. An extended technique for order preference by similarity to an ideal solution (TOPSIS) with maximizing deviation method based on integrated weight measure for single-valued neutrosophic sets. Symmetry, 10(7), 236, 2018.Google ScholarCross Ref
- Selvachandran, G., Maji, P. K., Abed, I. E., and Salleh, A. R., Complex vague soft sets and its distance measures. Journal of Intelligent and Fuzzy Systems, 31(1), 55--68, 2016.Google ScholarDigital Library
- Selvachandran, G., Maji, P. K., Abed, I. E., and Salleh, A. R.. Relations between complex vague soft sets. Applied Soft Computing, 47, 438--448, 2016. Google ScholarDigital Library
- Selvachandran, G., Garg, H., Alaroud, M. H., and Salleh, A. R. Similarity measure of complex vague soft sets and its application to pattern recognition. International Journal of Fuzzy Systems, 1--14, 2018.Google ScholarCross Ref
- Selvachandran, G., Garg, H., and Quek, S. G., Vague Entropy Measure for Complex Vague Soft Sets. Entropy, 20(6), 403, 2018.Google ScholarCross Ref
- Selvachandran, G., and Singh, P. K., Interval-valued Complex Fuzzy Soft Set and Its Application. International Journal for Uncertainty Quantification, 8(2), 2018.Google Scholar
- Selvachandran, G., Pal, M., Alhawari, T. A. A., and Salleh, A. R., Interval-valued complex fuzzy sets and its application to the malaysian economy. International Journal of Fuzzy System Applications (IJFSA), 7(1), 22--31, 2018. Google ScholarDigital Library
- Suuml; kran, Y., and Guuml; lguuml; n, O. Z., Fuzzy logic methodology and multiple regressions for residential real-estates valuation in urban areas. Scientific Research and Essays, 6(12), 2431--2436, 2011.Google Scholar
- Hepşen, A., and Vatansever, M., Forecasting future trends in Dubai housing market by using Box-Jenkins autoregressive integrated moving average. International Journal of Housing Markets and Analysis, 4(3), 210--223, 2011.Google ScholarCross Ref
- Vishwakarma, V., Forecasting Real Estate Business: Empirical Evidence from the Canadian Market, Global Journal of Business Research, vol. 7 (3), pp. 1--14, 2013.Google Scholar
- Chiarazzo, V., Caggiani, L., Marinelli, M., and Ottomanelli, M., A neural network based model for real estate price estimation considering environmental quality of property location, Transportation Research Procedia, 3, 810--817, 2014.Google ScholarCross Ref
- Benjamin, J., Guttery, R., and Sirmans, C. F., Mass appraisal: an introduction to multiple regression analysis for real estate valuation. Journal of Real Estate Practice and Education, 7(1), 65--77, 2004.Google ScholarCross Ref
- Ahn, J. J., Byun, H. W., Oh, K. J., and Kim, T. Y., Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting. Expert Systems with Applications, 39(9), 8369--8379, 2012. Google ScholarDigital Library
- Božić, B., Milićević, D., Pejić, M., and Marošan, S,. The use of multiple linear regression in property valuation. Geonauka, 1(1), 41--45, 2013.Google ScholarCross Ref
- Teck-Hong, T., Neighborhood preferences of house buyers: the case of Klang Valley, Malaysia. International Journal of Housing Markets and Analysis, 4(1), 58--69, 2011.Google ScholarCross Ref
- Hui, E. C., Zhong, J. W., and Yu, K. H., The impact of landscape views and storey levels on property prices. Landscape and urban planning, 105(1--2), 86--93, 2012.Google Scholar
Index Terms
- Valuation of Real Estate: A Multiple Regression Approach
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