Optimizing Generalized Linear Models for Real Estate Investment Risk: A Hybrid Genetic Algorithm and IRLS Approach
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Keywords:
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Generalized Linear Model, Return on Investment, Macroeconomic Indicators, Nigerian Stock Exchange, Investment Risk
Abstract
Generalized Linear Models (GLMs) serve as a powerful extension of traditional linear regression, enhancing statistical analysis capabilities. This study employs a GLM to forecast the Return on Investment (ROI) within Nigeria’s real estate and construction sectors, utilizing a comprehensive dataset that includes data from the Nigerian Stock Exchange (NSE) and macroeconomic indicators from the National Bureau of Statistics (NBS) and the Central Bank of Nigeria (CBN), spanning 2017 to 2023. The model integrates critical macroeconomic indicators, including Gross Domestic Product (GDP) growth, the Consumer Price Index (CPI), Interest Rates (IR), and the Unemployment Rate (UR), while also considering interaction effects and non-linear terms to enhance predictive accuracy. Two methods were used to assess the GLM: Genetic Algorithms (GLM-GA) and Iteratively Reweighted Least Squares (GLM-IRLS), both of which revealed significant insights. Adjusted R2 values ranged from 0.68 to 0.73, with the highest in 2019 (0.73) and the lowest in 2021 (0.68). The Bayesian Information Criterion (BIC) exhibited variation, with values between 365.50 in 2019 and 386.30 in 2021, indicating differing model efficiency across years. Investment risk metrics, such as Value at Risk (VaR) and Conditional Value at Risk (CVaR), showed upward trends, with VaR increasing from 7.50 in 2017 to 9.20 in 2022, and CVaR rising from 7.00 to 8.70 in the same period, reflecting heightened risk exposure. The findings underscore the sensitivity of ROI predictions to macroeconomic conditions and highlight the challenges of forecasting amid economic volatility. This study emphasizes the substantial impact of macroeconomic factors on ROI and the necessity of considering these variables in investment risk assessments.
References
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Maghawry, A., R. Hodhod, Y. Omar, and M. Kholief (2021). An Approach for Optimizing Multi-objective Problems Using Hybrid Genetic Algorithms. Soft Computing, 25; 389–405.
Mahmood, T., A. Iqbal, S. A. Abbasi, and M. Amin (2022). Efficient GLM-based Control Charts for Poisson Processes. Quality and Reliability Engineering International, 38(1); 389–404.
Marzjarani, M. (2019). Iterative Approaches to Handling Heteroscedasticity with Partially Known Error Variances. International Journal of Probability and Statistics, 8(2); 159–171.
Meier, R. and R. Danzinger (2022). Personalized Portfolio Optimization Using Genetic (AI) Algorithms. Springer International Publishing, pages 199–214.
Moiz, A. A., P. Pal, D. Probst, Y. Pei, Y. Zhang, S. Som, and J. Kodavasal (2018). A Machine Learning-genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-performance Computing. SAE International Journal of Commercial Vehicles, 11(2018-01-0190); 291–306.
Moore, E. A. (2019). Addressing Housing Deficit in Nigeria: Issues, Challenges and Prospects. Economic and Financial Review, 57(4); 15.
Mudiangombe, B. M. and J. W. M. Mwamba (2023). Dependence Structure and Time–frequency Impact of Exchange Rates on Crude Oil and Stock Markets of BRICS Countries: Markov-switching-based Wavelet Analysis. Journal of Risk and Financial Management, 16(7); 319.
Naeem, M. A. (2024). Navigating Median and Extreme Volatility in Stock Markets: Implications for Portfolio Strategies. International Review of Economics & Finance; 103507.
Olanrewaju, O. I., N. Chileshe, S. A. Babarinde, and M. Sandanayake (2020). Investigating the Barriers to Building Information Modeling (BIM) Implementation within the Nigerian Construction Industry. Engineering, Construction and Architectural Management, 27(10); 2931–2958.
Sang, B. (2021). Application of Genetic Algorithm and BP Neural Network in Supply Chain Finance under Information Sharing. Journal of Computational and Applied Mathematics, 384; 113170.
Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6(2); 461–464.
Smith, R. A., K. S. Andrews, D. Brooks, S. A. Fedewa, D. Manassaram-Baptiste, D. Saslow, and R. C. Wender (2019). Cancer Screening in the United States, 2019: A Review of Current American Cancer Society Guidelines and Current Issues in Cancer Screening. CA: A Cancer Journal for Clinicians, 69(3); 184–210.
Traunmüller, R., A. Murr, and J. Gill (2015). Modeling Latent Information in Voting Data with Dirichlet Process Priors. Political Analysis, 23(1); 1–20.
Tudose, M. and S. Avasilcai (2021). Financial Performance Management and Economic Cycle Variations: Evidence for the Textile Industry. In IOP Conference Series: Materials Science and Engineering, volume 1169, page 012016.
Tziralis, G., K. Kirytopoulos, A. Rentizelas, and I. Tatsiopoulos (2009). Holistic Investment Assessment: Optimization, Risk Appraisal, and Decision Making. Managerial and Decision Economics, 30(6); 393–403.
Yılmaz, B. et al. (2020). Forecasting House Prices in Turkey: GLM, VAR, and Time Series Approaches. Journal of Business Economics and Finance, 9(4); 274–291.
Zareie, B., J. Poorolajal, A. Roshani, and M. Karami (2023). Outbreak Detection Algorithms Based on Generalized Linear Model: A Review with New Practical Examples. BMC Medical Research Methodology, 23(1); 235.
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Abdullahi, M. and K. Bala (2018). Analysis of the Causality Links Between the Growth of the Construction Industry and the Growth of the Nigerian Economy. Journal of Construction in Developing Countries, 23(1); 103–113.
Abubakar, H., M. Misiran, A. A. I. Sayed, and A. B. Karaye (2024a). Optimization of Weibull Distribution Parameters with Application to Short-Term Risk Assessment and Strategic Investment Decision-Making. Statistics, Optimization & Information Computing, 12; 0–25.
Abubakar, H., M. Misiran, and A. I. A. Sayed (2024b). Estimation of Shifted Weibull Distribution Parameters Using Optimization Algorithms for Optimal Investment Decision-Making. Franklin Open, 8; 100152.
Adams, J. and L. Brown (2021). Predictive Modeling in Economics. Journal of Economic Studies, 45(3); 233–250.
Alam, I. and Y. Ali (2023). Studying the Effects of Türkiye Earthquake Disaster and Its Impact on the Real Estate Industry: A Risk Analysis Based on Input-Output & Non-Linear Optimization Models. International Journal of Disaster Risk Reduction, 96; 103920.
Ametefe, F. K., P. A. Brenni, and F. Viruly (2023). Sustainable Real Estate Investment and Finance in Developing Economies: Challenges and Opportunities, Chapter 17, pages 262–285.
Anderson, D. R. and K. P. Burnham (2002). Avoiding Pitfalls When Using Information-Theoretic Methods. The Journal of Wildlife Management; 912–918.
Bhoskar, M. T., M. O. K. Kulkarni, M. N. K. Kulkarni, M. S. L. Patekar, G. Kakandikar, and V. Nandedkar (2015). Genetic Algorithm and Its Applications to Mechanical Engineering: A Review. Materials Today: Proceedings, 2(4-5); 2624–2630.
Bouchouicha, R. and Z. Ftiti (2012). Real Estate Markets and the Macroeconomy: A Dynamic Coherence Framework. Economic Modelling, 29(5); 1820–1829.
Bunyaminu, A. and M. Issah (2012). The Sustainability of Micro-Finance Companies: Generalized Linear Modeling (GLM) Approach. International Research Journal of Finance and Economics, 95; 6–11.
Chit, M. M., M. Rizov, and D. Willenbockel (2010). Exchange Rate Volatility and Exports: New Empirical Evidence from the Emerging East Asian Economies. World Economy, 33(2); 239–263.
Doe, J. (2022). Impact of Macroeconomic Variables on Investment. International Journal of Finance, 39(1); 112–130.
Fortes, M. Z., V. H. Ferreira, and A. P. F. Coelho (2013). The Induction Motor Parameter Estimation Using Genetic Algorithm. IEEE Latin America Transactions, 11(5); 1273–1278.
Gad, A. M. and M. E. Qura (2016). Regression Estimation in the Presence of Outliers: A Comparative Study. International Journal of Probability and Statistics, 5(3); 65–72.
Hasan, M. B., M. A. R. Kabir, M. R. Amin, and M. Mahi (2022). Effect of Macroeconomic Variables on Islamic and Conventional Stock Indices: Fresh Evidence from Bangladesh. The Journal of Developing Areas, 56(2); 197–220.
Hastie, T. J. and D. Pregibon (2017). Generalized Linear Models. Routledge, pages 195–247.
Idris, A. A. and S. S. R. Muhammad (2022). A Simulation Study on the Simulated Annealing Algorithm in Estimating the Parameters of Generalized Gamma Distribution. Science and Technology Indonesia, 7(1); 84–90.
Ivascu, L., A. E. Domil, A. E. Artene, O. Bogdan, V. Burca, and C. Pavel (2022). Psychological and Behavior Changes of Consumer Preferences During COVID-19 Pandemic Times: An Application of GLM Regression Model. Frontiers in Psychology, 13; 879368.
Johnson, C. and B. Williams (2020). Gender and Political Leadership in a Time of COVID. Politics & Gender, 16(4); 943–950.
Jones, M. and A. Smith (2021). Economic Uncertainty and Risk Metrics. Global Economic Review, 28(2); 155–170.
Junkes, M. B., A. P. Tereso, and P. S. Afonso (2015). The Importance of Risk Assessment in the Context of Investment Project Management: A Case Study. In Procedia Computer Science, volume 64, pages 902–910.
Kırdök, F. E. (2012). Dynamic Relationship Between Macroeconomic Variables and Returns on Turkish Real Estate Investment Trusts. Master’s thesis, Bilkent University, Turkey.
Li, J., B. Alvarez, J. Siwabessy, M. Tran, Z. Huang, R. Przeslawski, L. Radke, F. Howard, and S. Nichol (2017). Application of Random Forest, Generalized Linear Model and Their Hybrid Methods with Geostatistical Techniques to Count Data: Predicting Sponge Species Richness. Environmental Modelling & Software, 97; 112–129.
Liu, X., Z. Zhang, P. F. Sousa, C. Chen, M. Ouyang, Y. Wei, Y. Liang, Y. Chen, and C. Zhang (2014). Selective Iteratively Reweighted Quantile Regression for Baseline Correction. Analytical and Bioanalytical Chemistry, 406; 1985–1998.
Lynn, D. J. (2010). Emerging Market Real Estate Investment: Investing in China, India, and Brazil, volume 196. John Wiley & Sons.
Maghawry, A., R. Hodhod, Y. Omar, and M. Kholief (2021). An Approach for Optimizing Multi-objective Problems Using Hybrid Genetic Algorithms. Soft Computing, 25; 389–405.
Mahmood, T., A. Iqbal, S. A. Abbasi, and M. Amin (2022). Efficient GLM-based Control Charts for Poisson Processes. Quality and Reliability Engineering International, 38(1); 389–404.
Marzjarani, M. (2019). Iterative Approaches to Handling Heteroscedasticity with Partially Known Error Variances. International Journal of Probability and Statistics, 8(2); 159–171.
Meier, R. and R. Danzinger (2022). Personalized Portfolio Optimization Using Genetic (AI) Algorithms. Springer International Publishing, pages 199–214.
Moiz, A. A., P. Pal, D. Probst, Y. Pei, Y. Zhang, S. Som, and J. Kodavasal (2018). A Machine Learning-genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-performance Computing. SAE International Journal of Commercial Vehicles, 11(2018-01-0190); 291–306.
Moore, E. A. (2019). Addressing Housing Deficit in Nigeria: Issues, Challenges and Prospects. Economic and Financial Review, 57(4); 15.
Mudiangombe, B. M. and J. W. M. Mwamba (2023). Dependence Structure and Time–frequency Impact of Exchange Rates on Crude Oil and Stock Markets of BRICS Countries: Markov-switching-based Wavelet Analysis. Journal of Risk and Financial Management, 16(7); 319.
Naeem, M. A. (2024). Navigating Median and Extreme Volatility in Stock Markets: Implications for Portfolio Strategies. International Review of Economics & Finance; 103507.
Olanrewaju, O. I., N. Chileshe, S. A. Babarinde, and M. Sandanayake (2020). Investigating the Barriers to Building Information Modeling (BIM) Implementation within the Nigerian Construction Industry. Engineering, Construction and Architectural Management, 27(10); 2931–2958.
Sang, B. (2021). Application of Genetic Algorithm and BP Neural Network in Supply Chain Finance under Information Sharing. Journal of Computational and Applied Mathematics, 384; 113170.
Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6(2); 461–464.
Smith, R. A., K. S. Andrews, D. Brooks, S. A. Fedewa, D. Manassaram-Baptiste, D. Saslow, and R. C. Wender (2019). Cancer Screening in the United States, 2019: A Review of Current American Cancer Society Guidelines and Current Issues in Cancer Screening. CA: A Cancer Journal for Clinicians, 69(3); 184–210.
Traunmüller, R., A. Murr, and J. Gill (2015). Modeling Latent Information in Voting Data with Dirichlet Process Priors. Political Analysis, 23(1); 1–20.
Tudose, M. and S. Avasilcai (2021). Financial Performance Management and Economic Cycle Variations: Evidence for the Textile Industry. In IOP Conference Series: Materials Science and Engineering, volume 1169, page 012016.
Tziralis, G., K. Kirytopoulos, A. Rentizelas, and I. Tatsiopoulos (2009). Holistic Investment Assessment: Optimization, Risk Appraisal, and Decision Making. Managerial and Decision Economics, 30(6); 393–403.
Yılmaz, B. et al. (2020). Forecasting House Prices in Turkey: GLM, VAR, and Time Series Approaches. Journal of Business Economics and Finance, 9(4); 274–291.
Zareie, B., J. Poorolajal, A. Roshani, and M. Karami (2023). Outbreak Detection Algorithms Based on Generalized Linear Model: A Review with New Practical Examples. BMC Medical Research Methodology, 23(1); 235.
İyit, N., H. Yonar, A. Yonar, N. İyit, H. Dogan, and H. Akgul (2021). An Application of Generalized Linear Model Approach on Econometric Studies. Research & Reviews in Science and Mathematics-II.
Authors
Abubakar, H., Misiran, M., Mansor, K. H., & Sabri, S. R. M. (2025). Optimizing Generalized Linear Models for Real Estate Investment Risk: A Hybrid Genetic Algorithm and IRLS Approach. Science and Technology Indonesia, 10(1), 88–100. https://doi.org/10.26554/sti.2025.10.1.88-100
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