Enhancing House Rent Prediction Using A Stacking Ensemble Model for Sustainable Community Development
Abiola, Oladimeji Arowolo, Rotimi, Israel Taye, Odufewa, Jesufunminire Emmanuel
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Source: Crossref
Published: Oct 12, 2026
DOI: 10.47191/ijmcr/v14i10.03
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House rent estimation remains a challenge due to inconsistent pricing methods, reliance on manual valuation, and the absence of intelligent systems for determining fair rent values. Traditional approaches used by house agents and property owners often result in price variations and inaccurate rent estimations. This study developed a House Rent Prediction System using stack ensemble to estimate annual house rent values based on housing characteristics. The dataset used for the study was obtained from house agents and secondary sources and was preprocessed through data cleaning, encoding, and feature scaling using StandardScaler. The developed system was implemented using Streamlit and included functionalities such as manual prediction, property visualization, house comparison, prediction history, market analytics, and property carousel display. Model evaluation was carried out using MAE, MSE, RMSE, R² Score, and K-Fold Cross Validation. The results showed that the developed system was effective in predicting house rent values and can support intelligent housing valuation and decision-making in community development.
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