IIARD International Institute of Academic Research and Development

RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY (RJPST )

E-ISSN 2579-0536
P-ISSN 2695-2696
VOL. 6 NO. 3 2023
DOI: 10.56201/rjpst.v6.no3.2023.pg204.225

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Awareness of Artificial Intelligence Technologies for Residential Property Valuation in Lagos Metropolis, Nigeria

Amos Olaolu ADEWUSI and Oluwakemi Faith GBADEBO

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Abstract

Property valuation is a core aspect of estate surveying and valuation practices, providing crucial estimates for real estate stakeholders including developers, institutional lenders, insurance companies among others. Traditional valuation methods often fall short in accurately estimating property values, leading advanced economies to promote the use of Artificial Intelligence Technologies (AIT). While significant research has been conducted on the awareness and adoption of AIT in developed countries, there is limited research on this topic in developing countries. This study aims to assess the level of awareness of AIT among professionals involved in property valuation in Lagos, Nigeria. Data for the study were collected from estate firms across Lagos metropolis and analyzed using descriptive statistics and Chi-square tests. Sixteen types of AI technologies were identified, and six training algorithms were evaluated. The results reveal a general lack of awareness of AIT among estate valuers practicing in Lagos metropolis. Specifically, for CatBoost, XGBoost, LGBM, and Random Forest, there is no significant relationship between years of professional qualification or educational qualification and awareness levels of AIT, as indicated by Pearson Chi-Square tests with p-values > 0.05. In addition, symmetric measures (Phi and Cramer's V values) indicate weak to very weak associations between years of professional experience, educational qualifications, and awareness levels of the selected AI technologies. This highlights a significant gap in the training of valuers in the study area. The findings offer valuable insights for property professionals, real estate investors, and policymakers, suggesting a need for enhanced training and awareness programs to bridge this gap.

keywords:

Artificial Intelligence (AI), Property value, Property valuation

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References:

Abidoye, R. B. (2017). Towards property valuation accuracy: a comparison of hedonic pricing
model and artificial neural network. Ph.D thesis submitted to the department of building
and real estate, the Hong Kong Polytechnic University
Abidoye, R. B., & Chan, A. P. (2017). Valuers’ receptiveness to the application of artificial
intelligence in property valuation. Pacific Rim Property Research Journal, 23(2), 175
193.
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.
Adetunji, A. B., Akande, O. N., Ajala, F. A., Oyewo, O., Akande, Y. F., & Oluwadara, G. (2022).
House price prediction using random forest machine learning technique. Procedia
Computer Science, 199, 806-813.
Adewusi, A.O. (2022), Modelling Residential Property Prices Using Artificial Intelligence
Technologies in Lagos Metropolis, Nigeria. World Journal of Innovation and Mogern
Technology, 5(1), 20- 43.
Amri, S., & Tularam, G. A. (2012). Performance of multiple linear regression and nonlinear neural
networks and fuzzy logic techniques in modeling house prices. Journal of
Mathematics and Statistics, 8(4), 419-434.
Antipov, E. A., & Pokryshevskaya, E. B. (2012). Mass appraisal of residential apartments: An
application of Random Forest for valuation and a CART-based approach for model
diagnostics. Expert systems with applications, 39(2), 1772-1778.
Armugam, M., Najib, R. M., & David, M. J. (2007). The existence of automated valuation
models (AVMS) among valuation firms in Malaysia. The Surveyors, 1, 1-14.
Arnott, D., Pervan, G., & Dodson, G. (2005). Who pays for decision support systems research?
Review, directions, and issues. Communications of the Association for Information
Systems, 16(1), 17.
Azmi, A. S. M., Nawawi, A. H., Latif, S. N. F. A., & Ling, N. L. F. J. (2013). Investigating the
Learning Process amongst Young Practicing Property Valuers. In International Real
Estate Research Symposium (IRERS2014), INSPEN, Kuala Lumpur:(yet to publish).
Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system.
In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery
and data mining (pp. 785-794).
Chi Man Hui, E., Muk Fai Lau, O., & Kak Keung Lo, T. (2009). Deciphering real estate investment
decisions through fuzzy logic systems. Property Management, 27(3), 163-177.
Choi, S. W., Ko, T., Hong, K. J., & Kim, K. H. (2019). Machine learning-based prediction of
Korean triage and acuity scale level in emergency department patients. Healthcare
informatics research, 25(4), 305.
Del Giudice, V., De Paola, P., & Cantisani, G. B. (2017). Valuation of real estate investments
through Fuzzy Logic. Buildings, 7(1), 26.
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical
features support. arXiv preprint arXiv:1810.11363.
French, N. (2004). The valuation of specialized property: A review of valuation methods. Journal
of Property Investment & Finance, 22(6), 533-541.
Gómez-Ríos, A., Luengo, J., & Herrera, F. (2017). A study on the noise label influence in
boosting algorithms: AdaBoost, GBM and XGBoost. In Hybrid Artificial Intelligent
Systems: 12th International Conference, HAIS 2017, La Rioja, Spain, June 21-23, 2017,
Proceedings 12 (pp. 268-280). Springer International Publishing.
Guan, J., Zurada, J., & Levitan, A. (2008). An adaptive neuro-fuzzy inference system-based
approach to real estate property assessment. Journal of Real Estate Research, 30(4), 395
422.
Hassan, Y. (2019). Property value assessment using artificial neural networks, hedonic regression
and nearest neighbors regression methods. Selçuk Üniversitesi Mühendislik, Bilim ve
Teknoloji Dergisi, 7(2), 387-404.
Hu, G. C. (1964). Variable-factors theory of bearing capacity. Journal of the Soil Mechanics and
Foundations Division, 90(4), 85-95.
Ho, W. K., Tang, B. S., & Wong, S. W. (2021). Predicting property prices with machine learning
algorithms. Journal of Property Research, 38(1), 48-70.
Ibrahim, A., Muhammed, M. M., Sowole, S. O., Raheem, R., & Abdulaziz, R. O. (2020).
Performance of CatBoost classifier and other machine learning methods.
Kamire, A., Chaphalkar, N., & Sandbhor, S. (2021). Real Property Value Prediction Capability
Using Fuzzy Logic and ANFIS. In Proceedings of the International Conference on Smart
Data Intelligence (ICSMDI 2021).
Kang, J. K., Cho, K. H., Kim, S. B., & Choi, N. C. (2020). Artificial neural network modeling
for prediction of dynamic changes in solution from bioleaching by indigenous acidophilic
bacteria. Applied Sciences, 10(21), 7569.
Khalafallah, A. (2008). Neural network based model for predicting housing market
performance. Tsinghua Science and Technology, 13(S1), 325-328.
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (Vol. 26, p. 13). New York:
Springer.
Kumar, S., Sharma, A., Reddy, B. K., Sachan, S., Jain, V., & Singh, J. (2022). An intelligent model
based on integrated inverse document frequency and multinomial Naive Bayes for current
affairs news categorization. International journal of system assurance engineering and
management, 13(3), 1341-1355.
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 (p. 97).
Mohd, T., Jamil, N. S., Johari, N., Abdullah, L., & Masrom, S. (2019). An overview of real estate
modelling techniques for house price prediction. In Charting a Sustainable Future of
ASEAN in Business and Social Sciences: Proceedings of the 3?? International Conference
on the Future of ASEAN (ICoFA) 2019—Volume 1 (pp. 321-338). Springer Singapore.
Mora-Esperanza, J. G. (2004). Artificial intelligence applied to real estate valuation: An example
for the appraisal of Madrid. Catastro, 1, 255-265.
Odunfa, V., Fateye, T., & Adewusi, A. (2021). Application of artificial intelligence (ai) approach
to African real estate market analysis opportunities and challenges. Advances in
Multidisciplinary & Scientific Research Journal Publication, 29, 121-132.
Oyedeji, O. J., Oyeniran, O. M., & Majekodunmi, S. O. (2022). Comparative Assessment of
Radom Forest, SVC and Cat Boost Performances as Property Price Forecasting
Models. Mathematical Statistician and Engineering Applications, 71(4), 1283-1289.
Ozcelik (2023) land valuation with fuzzy logic in artificial intelligence application.
Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., & French, N. (2003). Real estate appraisal:
a review of valuation methods. Journal of property investment & finance, 21(4), 383-401.
Pitts, W. (1943). The linear theory of neuron networks: The dynamic problem. The bulletin of
mathematical biophysics, 5, 23-31.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost:
unbiased boosting with categorical features. Advances in neural information
processing systems, 31.
Sarip, A. G., & Hafez, M. B. (2015). Fuzzy logic application for house price
prediction. International Journal of Property Sciences (E-ISSN: 2229-8568), 5(1).
Sibindi, R., Mwangi, R. W., & Waititu, A. G. (2023). A boosting ensemble learning based hybrid
light gradient boosting machine and extreme gradient boosting model for predicting house
prices. Engineering Reports, 5(4), e12599.
Silver, M. S. (1991). Systems that support decision makers: description and analysis. John Wiley
& Sons, Inc.
Siregar, M. U., Hardjita, P. W., Asdin, F. A., Wardani, D., Wijayanto, A., Yunitasari, Y., &
Anshari, M. (2022, November). Housing Price Prediction Using a Hybrid Genetic
Algorithm with Extreme Gradient Boosting. In Proceedings of the 2022 International
Conference on Computer, Control, Informatics and Its Applications (pp. 296-300).
Ta

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