The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market

Abstract

The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy.

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“…This is a moment in history when the interaction between microeconomic and macroeconomic events is extremely strong because of the globally impactful events in the recent years of the COVID-19 pandemic [30] and the Ukraine war outbreak [31]. Several consequences have involved the financial [32][33][34] and energetic [35][36][37] sectors, with evident repercussions on the real estate sector, such as higher offer rigidity, changes in demand preferences, and increases in the costs of construction, therefore ultimately affecting property prices [38].…”