This project aims to develop an Automated Valuation Model (AVM) for real estate property value prediction using machine learning techniques. Traditional property valuation methods, such as Price per Square Foot, Comparative Market Analysis (CMA), and Appraisal, often require significant manpower, are time-consuming, and may lack generalization. This project addresses these issues by implementing machine learning algorithms to automate and improve the accuracy of property value estimations.
By leveraging data from various property features and locations, the model predicts the median house value to assist buyers, sellers, and real estate professionals in making informed decisions quickly and efficiently.
- Goal: Develop a machine learning model to predict property values based on real estate data.
- Objectives:
- Automate property valuation to reduce resource dependency.
- Provide accurate and immediate property value estimates.
- Minimize errors and improve generalization across different properties and locations.
- Deploy the model as a tool for real estate professionals and potential buyers.
The project follows a structured machine learning pipeline to ensure accurate and reliable predictions:
Gather property-related datasets containing variables such as:
- Location (Latitude, Longitude, Neighborhood)
- Property size (Square Footage, Bedrooms, Bathrooms)
- Property condition (Age, Renovations)
- Market factors (Local Economic Indicators, Comparable Property Prices)
- Handle missing values, duplicated data, and outliers.
- Standardize numerical features and encode categorical variables.
- Perform exploratory data analysis (EDA) to identify trends and patterns.
- Correlation analysis to identify the most relevant features.
- Create new features, such as price per square foot, age of property, etc.
- Apply feature selection techniques (Filter Methods, Wrapper Methods, Embedded Methods).
- Test various regression models to capture complex relationships between features and property values:
- Linear Regression
- Decision Tree Regressor
- Random Forest Regressor
- Gradient Boosting Regressor
- XGBoost, LightGBM
- Split the data into training and testing sets (e.g., 80/20 split).
- Train models on the training set and validate performance on the test set.
Evaluate model performance using the following metrics:
- Mean Absolute Error (MAE): Measures average absolute difference between predicted and actual prices.
- Root Mean Squared Error (RMSE): Penalizes larger errors, sensitive to outliers.
- Mean Absolute Percentage Error (MAPE): Evaluates the relative performance of the model.
- R-Squared (R²): Indicates how well the model explains the variance in the data.
- Perform hyperparameter tuning using Grid Search, Random Search, or Bayesian Optimization to optimize model performance.
- Deploy the model as an automated system (web application or API) for real-time property valuation.
- Provide visualizations and insights into the most influential features affecting property value.
The following metrics are used to assess model performance:
- Mean Absolute Error (MAE):
- Measures the average absolute difference between predicted and actual prices.
- Lower values indicate better performance.
- Root Mean Squared Error (RMSE):
- Penalizes larger errors more heavily, helping identify poor predictions.
- Mean Absolute Percentage Error (MAPE):
- Useful for understanding percentage-based errors in predictions.
- R-Squared (R²):
- Represents the proportion of variance explained by the model.
- Higher values (closer to 1) indicate a better fit.
- Check for missing values, duplicated entries, and inconsistencies.
- Standardize data formats and correct any typing errors.
- Remove or impute missing data.
- Visualize data distributions, correlations, and outliers using histograms, box plots, and scatter plots.
- Identify trends and potential predictors of property value.
- Feature Correlation Analysis: Identify highly correlated features to avoid multicollinearity.
- Feature Selection: Choose the most important features that contribute to accurate predictions.
- Feature Creation: Generate new features such as property age, neighborhood average price, and renovation status.
the cross validation executed to find best performed of 5 common model to be use benchmarked model with have the lowest metric evaluation were further improved by implementing hyper parameter tuning. two best model and pick the best performer to be focused on.
compared the before tuned model to after tuned model with the efforts of parameter inplace to the hyper parameter tuning herewith result obtained It can seen from our testing, that the tuning have better result in terms of RMSE, MAE and MAPE respectively.
the error prediction were lowered by the efforts of hyperparameter tuning.
RMSE = 41482.32 --> 40445.69 = 1036.63 or equivalent to 2.5 % better after tuning
MAE = 28642.96 --> 27568.58 = 1074.38 or equivalent to 3.75 % better afer tuning
while the model have 15.8% tendency to have false prediction, which were still acceptable and have reasonable in our scenario, surely data utilization were still high from previous 14448 to after cleaning 13324 equivalent to 92.22% data preserved for the model
As we have seen XGBoost model with aforementioned hyper tuned parameter
The model built on average, the predicted median house values deviate from the actual values by approximately $40,445.70. according to RMSE. And the model's predictions are off by around $27,568.60 according to MAE.
A MAPE of around 15-20% is generally considered acceptable in many forecasting scenario. This with obtained percentage 15.87%, error margin suggests that the model's predictions could be higher or lower than actual house values.
Thing to keep in mind, and limitation of model built were not considered "The model's accuracy and results may be influenced by external factors such as the condition of the actual property, existing furniture, interior design, and other environmental elements. Discrepancies between the model and reality may occur due to these variations."