Neighbourhood Component Regression Approach for Housing Unit Price Prediction
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Predicting housing unit price (HUP) is important for potential buyers and investors to make informed decisions. This study proposes a novel HUP prediction model based on neighbourhood component regression (NCR). The proposed NCR model was compared with other competitive methods such as principal component regression (PCR), multiple linear regression (MLR), partial least squares regression (PLSR), and generalised linear model (GLM). When tested with real datasets, the proposed NCR method revealed prediction superiority over the four state-of-the-art methods (PCR, MLR, PLSR, and GLM). This was evident from the Mean Absolute Percentage Error (MAPE), Correlation Coefficient (R), Scatter Index (SI), and Percentage Root Mean Square Error (PRMSE) utilised as model evaluation metrics. The results revealed that the NCR model had the lowest MAPE (0.0977), SI (0.0011), PRMSE (0.1130), and highest R (0.9999) as compared with the other investigated methods. This confirms the proposed NCR method’s strength for efficient and reliable HUP prediction.
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