IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 2 2024
DOI: 10.56201/ijcsmt.v10.no2.May2024.pg35.52

---

Performance Comparison of Artificial Neural Networks and Hedonic Pricing model in Predicting Residential Property prices in Lagos Metropolis, Nigeria

Amos Olaolu Adewusi

---

Abstract

The search for techniques that can achieve better valuation estimates is a global issue, while much research has been done in the advanced countries, only a few researches are known to have been done in the area of adopting the advanced techniques in predicting property prices in Nigeria. The current paper compares the performance of the hedonic pricing model (HPM) and artificial neural networks (ANN) models in predicting the prices of residential properties in Lagos metropolis, Nigeria. Residential property prices and data for the variables affecting property prices were obtained from the databases of 53 firms of practicing Estate Surveyors and Valuers in the study area. A total of 3,079 datasets, encompassing property, neighborhood, and environmental-based features were gathered and employed in the research along with 19 explanatory factors. The entire dataset was split into training and testing at a ratio of 80% and 20% respectively to assess the prediction ability of the ANN and HPM. For both models, the performance evaluation metrics of R-squares, mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were computed and then compared. ANN model outperformed HPM model in predicting residential property prices in the Lagos metropolitan residential property market. The outcome of the research provides decision inputs for policymakers, investors in real estate, real estate professionals, and other stakeholders.

keywords:

Keywords not found

---

References:

Abidoye, R. B., & Chan, A. P. (2016). A survey of property valuation approaches in Nigeria. Property
Management, 34(5), 364-380.
Abidoye, R. B., & Chan, A. P. (2017). Critical review of hedonic pricing model application in
property price appraisal: A case of Nigeria. International Journal of Sustainable Built
Environment, 6(1), 250-259.
Abidoye, R. B., & Chan, A. P. (2018). Improving property valuation accuracy: A comparison of
hedonic pricing model and artificial neural network. Pacific Rim Property Research
Journal, 24(1), 71-83.
Adair, A. S., Berry, J. N., & McGreal, W. S. (1996). Hedonic modelling, housing submarkets
and
residential valuation. Journal of property Research, 13(1), 67-83.
Akinbogun, S., Jones, C., & Dunse, N. (2014). The property market maturity framework and its
application to a developing country: The case of Nigeria. Journal of real estate literature,
22(2), 217-232.
Alzain, E., Alshebami, A. S., Aldhyani, T. H., & Alsubari, S. N. (2022). Application of artificial
intelligence for predicting real estate prices: The case of Saudiarabia. Electronics, 11(21), 3448.
Aminuddin, A. J., & Maimun, N. H. A. (2021). A review on the performance of house price index
models: hedonic pricing model vs artificial neural network
. International Journal of
Accounting, 7(39), 53-63.
Amri, S., & Tularam, G. A. (2012). Performance of multiple linear regression and nonlinear
neural
networks and fuzzy logic techniques in modelling house prices. Journal of
Mathematics
and Statistics, 8(4), 419-434.
Borst, R. A. (1991). Artificial neural networks: the next modelling/calibration technology for the

assessment community. Property Tax Journal, 10(1), 69-94.
Can, Y. S., Arnrich, B., & Ersoy, C. (2019). Stress detection in daily life scenariosusing smart phones
and wearable sensors: A survey. Journal of biomedical informatics, 92, 103139.
Canavarro, C., Caridad, J. M., & Ceular, N. (2010). Hedonic methodologies in the real estate

valuation. In Mathematical Methods in Engineering. InternationalSymposium, Coimbra.

21-24.
Cebula, R. J. (2009). The hedonic pricing model applied to the housing market of the city of

Savannah and its Savannah historic landmark district. Review of Regional Studies, 39(1),

9-22.
Cechin, A., Souto, A., & Gonzalez, M. A. (2000, November). Real estate value at Porto Alegre

city using artificial neural networks. In Proceedings. Vol. 1. SixthBrazilian Symposium

on Neural Networks (pp. 237-242).
Chau, K. W., & Chin, T. L. (2002). A critical review of literature on the hedonic price model.
International Journal for Housing Science and its applications, 27(2), 145-165.
Do, Q., & Grudnitski, G. (1992). A neural network analysis of the effect of age on
housing values.
Journal of Real Estate Research, 8(2), 253-264.
Famuyiwa, F., & Kayode Babawale, G. (2014). Hedonic values of physical infrastructure in house
rentals. Journal of Facilities Management, 12(3), 211-230.
Ge, J., Runeson, G., Lam, K., & Chan, K. (2004). Forecasting Hong Kong Property Prices:
Multiple Regression Method vs An Artificial Neural Network Approach. In World of
Construction
Project
Management
Conference.
World
of
Construction
Project
Management.
Ge, Y., Xiao, Y., Xu, Z., Zheng, M., Karanam, S., Chen, T., ... & Wu, Z. (2021). Apeek into the
reasoning of neural networks: Interpreting with structural visual concepts. In Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2195-2204.
Gowd, B. P., Jayasree, K., & Hegde, M. N. (2018). Comparison of artificial neural
networks
and
fuzzy logic approaches for crack detection in a beam like
structure. Int. J. Artif. Intell. Appl,
9(1), 35-51.
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., ... & Hussain, A. (2023).
Interpreting black-box models: A review on explainable artificial intelligence. Cognitive
Computation.
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., Ray, S., ... & Ray, S. (2021).
Evaluation of reflective measurement models. Partial Least Squares Structural Equation
Modeling (PLS-SEM) Using R: A Workbook, 75-90.
Hui, E. C., Chau, C. K., Pun, L., & Law, M. Y. (2007). Measuring the neighboring and
environmental effects on residential property value: Using spatial weighting matrix. Building
and environment, 42(6), 2333-2343.
Jim, C. Y., & Chen, W. Y. (2006). Impacts of urban environmental elements on residential housing
prices in Guangzhou (China). Landscape and urban planning, 78(4), 422-434.
Khumprom, P., & Yodo, N. (2019). A data-driven predictive prognostic model for lithium-ion
batteries based on a deep learning algorithm. Energies, 12(4), 660.
Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational
intelligence. Applied Soft Computing, 11(1), 443-448.
Kutasi, D., & Badics, M. C. (2016). Valuation methods for the housing market: Evidence from
Budapest. Acta Oeconomica, 66(3), 527-546.
Lam, K. C., Yu, C. Y. and Lam, K. Y. (2008). An artificial neural network and entropy model for
residential property price forecasting in Hong Kong. Journal of Property Research, 25(4),
321-342.
Lam, K. P., Höynck, M., Dong, B., Andrews, B., Chiou, Y. S., Zhang, R., ... & Choi, J. (2009).
Occupancy detection through an extensive environmental sensor network
in
an
open
plan office building.
Lenk, M. M., Worzala, E. M., & Silva, A. (1997). High?tech valuation: should artificial neural

networks bypass the human valuer? Journal of Property Valuation and Investment,
15(1), 8-26.
Lin, C., & Mohan, S. B. (2011). Effectiveness comparison of the residential property mass appraisal
methodologies in the USA. International Journal of Housing Markets and Analysis,
4(3),
224-243.
Limsombunchao, V. (2004). House price prediction: hedonic price model vs. artificial neural

network. American Journal of Applied Sciences, 1(3), 193-201.
Ma?kowska, A., & Uhruska, M. (2022). Factors affecting SMEs growth: the case of the real estate

valuation service industry. Oeconomia Copernicana, 13(1), 79-108.
Masías, V. H., Valle, M. A., Crespo, F., Crespo, R., Vargas, A., & Laengle, S. (2016). Property
valuation using machine learning algorithms: A study in a
Metropolitan-Area of Chile.
In
Selection at the AMSE Conferences, 97.
McCluskey, W. J., McCord, M., Davis, P. T., Haran, M., & McIlhatton, D. (2013).
Prediction

accuracy in mass appraisal: a comparison of modern
approaches.
Journal
of
Property

Research, 30(4), 239-265.
McGreal, S., Adair, A., McBurney, D., & Patterson, D. (1998). Neural networks: the prediction
of
residential values. Journal of Property Valuation and Investment, 16(1), 57 70.
Mimis, A., Rovolis, A., & Stamou, M. (2013). Property valuation with artificial neural network:
The case of Athens. Journal of Property Research, 30(2), 128-143.
Morano, P. I. E. R. L. U. I. G. I., Tajani, F. R. A. N. C. E. S. C. O., & Torre, C. M.
(2015).
Artificial intelligence in property valuations: an application of artificial neural networks to
housing appraisal. Advances in environmental science and energy planning, 23-29.
Ngoc, N. M., Tien, N. H., & Hieu, V. M. (2023). Enhancing efficiency of real estate brokerage activities
in Vietnam. International journal of business and globalization.
Ogunba, O.A., & C. A. A. (2007). The response of Nigerian valuers to increasing sophistication
in
investors’ requirements. Journal of Property Investment & Finance, 25(1), 43–61.
Owusu-Ansah, A. (2012). Examination of the determinants of housing values in urban Ghana and
implications for policy makers. Journal of African Real Estate Research, 2(1), 58-85.
Pagourtzi, E., Metaxiotis, K., Nikolopoulos, K., Giannelos, K., & Assimakopoulos, V. (2007). Real

estate valuation with arti

DOWNLOAD PDF

---

Back