Applying AI to Predict High-Cost Rental Prices: A Comparative Modelling Approach

Abstract:

Accurately predicting real estate values remains challenging in volatile and rapidly growing real estate markets, especially for expensive homes worldwide. Traditional statistical and economic models frequently miss the temporal dynamics and nonlinear dependencies affecting changes in property values. This study uses a large-scale, multi-market dataset to anticipate real estate values utilizing sophisticated deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Dense Neural Networks (DNN). To increase learning efficiency, data pretreatment techniques included time-series sequencing, categorical encoding, and normalization. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2) were used to assess the model's performance. The findings show that the LSTM model obtained the lowest average prediction error (MAE) and produced extremely accurate forecasts by effectively incorporating long-term temporal dependencies. According to the study's findings, deep learning gives a solid, scalable foundation for accurate property price predictions. It also has practical ramifications for urban planners, investors, and legislators in dynamic housing markets.

• Periodical: Advances in Science and Technology (Volume 181)
• Pages: 451-457
• DOI: https://doi.org/10.4028/p-pK3iuw
• Online since: August 2026
• Authors: Mohammad Amir Parvez*, Samad Dadvandipour
• Keywords: AI, Deep Learning, DNN, GRU, LSTM, Property Price Prediction, Real Estate Forecasting, Time-Series Analysis

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