Residential Tenants Classification: A Test of Performance of Five Selected Artificial Neural Networks training Algorithms
Keywords:
Artificial Neural Network, Training Algorithms, property investment
Abstract
Personal judgment of the property manager and traditional statistical methods are often used in reaching decisions on tenant selection, however, these methods have continued to be unreliable, inconsistent, and time-consuming. Artificial neural networks (ANNs) are known as powerful support tools in modeling unknown data relationships in decision making due to their abilities in pattern recognition of complex relationships, classification, prediction, forecasting, etc. While ANN has enjoyed continued application in many fields of endeavor, its application has been very limited in the fields of property management and investment.The current paper assesses the performance of ANN training algorithms in residential tenant classification with a view to choosing the best ANN training algorithm suitable for the classification of residential rental applicants in the Lagos Metropolis property market. Five ANN training algorithms are selected for analysis, namely, Levenberg-Marquardt (LM), Gradient Descent Backpropagation (GD), Resilient Back Propagation (RP), One Step Secant Back Propagation (OSS), and Gradients Descent with Momentum and Adaptive Rate Backpropagation (GDX). A total of 724 data samples of rental applications were obtained from the databases of the practicingproperty managers in the Nigerian property markets, the total samples were subdivided into 70%, 15%, and 15% for training, validation, and testing respectively. Test datasets (representing 15% of the total datasets) were used in evaluating the classification performance of the modeled (ANN training algorithms) The paper concludes that all the selected ANN training algorithms except GD produced good and efficient resultsin tenant classifications, however, GDX and OSS appear to be most suited for residential tenant classification in the Nigerian property market as they outperformed the other ANN training algorithms.The results provide decision inputs for professional real estate managers and cost–time saving frameworks for tenants selection.
References
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Lin, M.I.B., Groves, W.A., Freivalds, A., Lee, E.G. and Harper, M., (2012). Comparison of artificial neural network (ANN) and partial least squares (PLS) regression models for predicting respiratory ventilation: an exploratory study. European journal of applied physiology, 112(5),1603-1611.
Mustika, H.F., Syafiandini, A.F., Manik, L.P. and Rianto, Y., (2020). Evaluating Naïve Bayes Automated Classification for GBAORD. Computer Engineering and Applications Journal, 9(1), 29-37.
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Ogundari, K., (2017). Categorizing households into different food security states in Nigeria: the socio-economic and demographic determinants. Agricultural and Food Economics, 5(1), 8-19.
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Olawande, O.A., (2011). Harnessing real estate investment through proper tenant selection in Nigeria. Property Management, 4(2), 15-36.
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Qolomany, B., Al-Fuqaha, A., Gupta, A., Benhaddou, D., Alwajidi, S., Qadir, J. and Fong, A.C., (2019). Leveraging machine learning and big data for smart buildings: A comprehensive survey. IEEE Access, 7, 90316-90356.
Saji, S.A. and Balachandran, K., (2015); Comparative Study of various training algorithms of Artificial Neural Networks on Diabetes dataset. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 378-382.
Salleh, N.A., Johari, N. and Talib, Y., (2014). Identifying Variables Influencing Tenant Affordability to Pay Rent in Ipoh City Council Public Housing. In E3S Web of Conferences Vol. 3, EDP Sciences.
Sani, K.S. and Gbadegesin, J.T., (2015). A study of private rental housing market in Kaduna Metropolis, Nigeria. International Journal of Humanities and Social Science, 5(8), 173-183.
Sharif, M.S., Abbod, M., Krill, B., Amira, A. and Zaidi, H., (2011), Automatic PET volume analysis and classification based on ANN and BIC. In 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE) (pp. 565-570). IEEE.
Sokolova, M., Japkowicz, N. and Szpakowicz, S., (2006). Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In Australasian joint conference on artificial intelligence (pp. 1015-1021). Springer, Berlin, Heidelberg.
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Wong, Y.J., Arumugasamy, S.K. and Jewaratnam, J., (2018). Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization. Clean Technologies and Environmental Policy, 20(9), 1971-1986.
Wu, G. and Chang, E.Y., (2003), Class-boundary alignment for imbalanced dataset learning. In ICML 2003 workshop on learning from imbalanced data sets II, Washington, DC (49-56).
Wu, Y. and Ji, Q., (2015). Discriminative deep face shape model for facial point detection. International Journal of Computer Vision, 113(1), 37-53.
Yacim, J.A. and Boshoff, D.G.B., (2018). Impact of artificial neural networks training algorithms on accurate prediction of property values. Journal of Real Estate Research, 40(3), 375-418.
Yan, Q., Xu, L., Shi, J. and Jia, J., (2013). Hierarchical saliency detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1155-1162).
Yaqub, M.U. and Al-Ahmadi, M.S., (2016), Application of combined ARMA-neural network models to predict stock prices. In Proceedings of the 3rd Multidisciplinary International Social Networks Conference on Social Informatics (pp. 1-5).
Yau, C. and Davis, T., (1994). Using multi-criteria analysis for tenant selection. Decision Support Systems, 12(3), 233-244.
Zu, Q., Wu, T. and Wang, H., (2012). A multi-factor customer classification evaluation model. Computing and Informatics, 29(4), 509-520.
Akosa, J., (2017), Predictive accuracy: a misleading performance measure for highly imbalanced data. In Proceedings of the SAS Global Forum (pp. 2-5).
Aliyu, I.N., Abdulrahaman, M.D., Nwaokolo, B.O., and Abdulkareem, S.A., (2019). An Effective Breast Cancer Prediction and Classification Using Artificial Neural Network. Journal of Engineering and Technology, 2(2), 37-48.
Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B. and Pour, A.B., (2018). A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sensing, 10(6), 975-976.
Al-shayea, Q.k., (2011). Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues, 8(2). 150-154.
Amasaki, S. and Lokan, C., (2016); On applicability of fixed-size moving windows for ANN-based effort estimation. In 2016 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (IWSM-MENSURA) (213-218). IEEE.
Ayouche, S., Aboulaich, R., and Ellaia, R., (2017). Partnership credit scoring classification problem: a neural network approach. International Journal of Applied Engineering Research, 12(5), 693-704.
Bache, K. and Lichman, M., (2013). UCI machine learning repository.
Başaran, E., Cömert, Z., Şengür, A., Budak, Ü. Çelik, Y. and Toğaçar, M., (2019). Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 1-4). IEEE.
Boughorbel, S., Jarray, F. and El-Anbari, M., (2017). Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PloS one, 12(6).
Bradley, A.P., (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), 1145-1159.
Brownlee, J., (2017). Long short-term memory networks with python. Machine Learning Mastery. Retrieved from https://machinelearningmastery. com/deep-learning-with-python.
Bryant, C., (2019). Managing development in the Third World. Routledge.
Cömert, Z. and Kocamaz, A.F., (2017). Comparison of machine learning techniques for fetal heart rate classification. Acta Phys. Pol. A, 132(3), pp.451-454.
Dabara, D.I., Anthony, A.I., Olusegun, O.J., Eleojo, A.G. and Michael, A.O., (2017) Rent Default Factors in Residential Properties in Osogbo Metropolis Osun State, Nigeria. International Journal of Business and Management Studies, 6(1);61-68
Estabrooks A. and japkowicz, N., (2001), A mixture-of-experts frameworkfor learning from imbalanced data sets. In international symposium on intelligent Data Analysis (pp. 34-43). Springer, Berlin, Heidelberg.
Fonseca, M.B.B., Ferreira, F.A., Fang, W. and Jalali, M.S.,(2018). Classification and selection of tenants in residential real estate: a constructivist approach. International Journal of Strategic Property Management, 22(1), pp.1-11.
Fonseka, T.M., Bhat, V. and Kennedy, S.H., (2019). The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors. Australian & New Zealand Journal of Psychiatry, 53(10), 954-964.
Furick, M. T. (2006). Using Neural Networks to Develop a New Model to Screen Applicants for Apartment Rentals. Doctoral dissertation, Nova Southeastern University, Graduate School of Computer and Information Sciences.
Gbadegesin, J. and Oletubo, A., (2013). Analysis of tenant selection criteria in an emerging rental market. Global Journal of Management and Business Research Interdisciplinary, 13(7),1-12.
Gbadegesin, J.T. and Ojo, O., (2013), Ethnic bias in tenant selection in metropolitan Ibadan private rental housing market. Property management.6 (4), 29-45.
Gerritsen,L., (2017). Predicting student performance with Neural Networks (Doctoral dissertation, Doctoral dissertation, Tilburg University.)
Haque, M.N., Noman, N., Berretta, R. and Moscato, P., (2016). Heterogeneous ensemble combination search using genetic algorithm for class imbalanced data classification. PloS one, 11(1), 12-27.
Karim, H., Niakan, S.R. and Safdari, R., (2018). Comparison of neural network training algorithms for classification of heart diseases. IAES International Journal of Artificial Intelligence, 7(4), 185-103.
Kaya, A., Keceli, A.S., Catal, C., Yalic, H.Y., Temucin, H. and Tekinerdogan, B., (2019). Analysis of transfer learning for deep neural network based plant classification models. Computers and electronics in agriculture, 158, 20-29.
Khor, R.C., Nguyen, A., O’Dwyer, J., Kothari, G., Sia, J., Chang, D., Ng, S.P., Duchesne, G.M. and Foroudi, F., (2019). Extracting tumour prognostic factors from a diverse electronic record dataset in genito-urinary oncology. International journal of medical informatics, 12(1), 53-57.
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P. and Soricut, R., (2019). Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942.
Lau, E.T., Sun, L. and Yang, Q., (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 982-1000.
Lin, M.I.B., Groves, W.A., Freivalds, A., Lee, E.G. and Harper, M., (2012). Comparison of artificial neural network (ANN) and partial least squares (PLS) regression models for predicting respiratory ventilation: an exploratory study. European journal of applied physiology, 112(5),1603-1611.
Mustika, H.F., Syafiandini, A.F., Manik, L.P. and Rianto, Y., (2020). Evaluating Naïve Bayes Automated Classification for GBAORD. Computer Engineering and Applications Journal, 9(1), 29-37.
Narkhede, S., (2018). Understanding AUC-ROC Curve. Towards Data Science, 26.
Ogundari, K., (2017). Categorizing households into different food security states in Nigeria: the socio-economic and demographic determinants. Agricultural and Food Economics, 5(1), 8-19.
Olaopa, O.R. and Omodunbi, O., (2019). Politics of identity and crisis of nation building in Africa: the Nigerian experience. Journal of Nation-building & Policy Studies, 3(2), 45-65.
Olatoye, O., (2005). Borrowers’ Perception of the Degree of Cumbersomeness of Lenders Requirements in Housing Financing in Southwestern Nigeria. In Conference Proceedings, Brisbane, Australia.
Olawande, O.A., (2011). Harnessing real estate investment through proper tenant selection in Nigeria. Property Management, 4(2), 15-36.
Pogoson, A.I. and Saleh, M.U., (2019). Gender and Nigeria’s Internal Security Management. In Internal Security Management in Nigeria (pp. 633-647). Palgrave Macmillan, Singapore.
Qolomany, B., Al-Fuqaha, A., Gupta, A., Benhaddou, D., Alwajidi, S., Qadir, J. and Fong, A.C., (2019). Leveraging machine learning and big data for smart buildings: A comprehensive survey. IEEE Access, 7, 90316-90356.
Saji, S.A. and Balachandran, K., (2015); Comparative Study of various training algorithms of Artificial Neural Networks on Diabetes dataset. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 378-382.
Salleh, N.A., Johari, N. and Talib, Y., (2014). Identifying Variables Influencing Tenant Affordability to Pay Rent in Ipoh City Council Public Housing. In E3S Web of Conferences Vol. 3, EDP Sciences.
Sani, K.S. and Gbadegesin, J.T., (2015). A study of private rental housing market in Kaduna Metropolis, Nigeria. International Journal of Humanities and Social Science, 5(8), 173-183.
Sharif, M.S., Abbod, M., Krill, B., Amira, A. and Zaidi, H., (2011), Automatic PET volume analysis and classification based on ANN and BIC. In 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE) (pp. 565-570). IEEE.
Sokolova, M., Japkowicz, N. and Szpakowicz, S., (2006). Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In Australasian joint conference on artificial intelligence (pp. 1015-1021). Springer, Berlin, Heidelberg.
Tahir, M.A.U.H., Asghar, S., Manzoor, A. and Noor, M.A., (2019). A classification model for class imbalance dataset using genetic programming. IEEE, 7, pp.71013-71037.
Tharwat, A., (2018). Classification assessment methods. Applied Computing and Informatics.
Viejo, C.G., Fuentes, S., Howell, K., Torrico, D.D. and Dunshea, F.R., (2019). Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers. Physiology & behavior, 2(2),139-147.
Wong, Y.J., Arumugasamy, S.K. and Jewaratnam, J., (2018). Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization. Clean Technologies and Environmental Policy, 20(9), 1971-1986.
Wu, G. and Chang, E.Y., (2003), Class-boundary alignment for imbalanced dataset learning. In ICML 2003 workshop on learning from imbalanced data sets II, Washington, DC (49-56).
Wu, Y. and Ji, Q., (2015). Discriminative deep face shape model for facial point detection. International Journal of Computer Vision, 113(1), 37-53.
Yacim, J.A. and Boshoff, D.G.B., (2018). Impact of artificial neural networks training algorithms on accurate prediction of property values. Journal of Real Estate Research, 40(3), 375-418.
Yan, Q., Xu, L., Shi, J. and Jia, J., (2013). Hierarchical saliency detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1155-1162).
Yaqub, M.U. and Al-Ahmadi, M.S., (2016), Application of combined ARMA-neural network models to predict stock prices. In Proceedings of the 3rd Multidisciplinary International Social Networks Conference on Social Informatics (pp. 1-5).
Yau, C. and Davis, T., (1994). Using multi-criteria analysis for tenant selection. Decision Support Systems, 12(3), 233-244.
Zu, Q., Wu, T. and Wang, H., (2012). A multi-factor customer classification evaluation model. Computing and Informatics, 29(4), 509-520.
Published
2021-06-02
How to Cite
Adewusi, A., & Oguntokun, J. A. (2021). Residential Tenants Classification: A Test of Performance of Five Selected Artificial Neural Networks training Algorithms. IJO -International Journal of Business Management ( ISSN 2811-2504 ), 4(05), 15-31. Retrieved from http://ijojournals.com/index.php/bm/article/view/461
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