Rental Price Prediction In Brazil Using Machine Learning

Theocharous, Savvas (2020) Rental Price Prediction In Brazil Using Machine Learning. [Dissertation (University of Nottingham only)]

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This thesis investigates the joint role of the house-specific features mainly, with the sociodemographic

and macro-economic features on the rental price prediction. The research focusses on

the property rental market in Brazil utilizing a data-set with over 10.000 properties. By employing

machine learning algorithms such as the Linear Regression, the Random Forest Regressor and the

Support Vector Regression, we aim to find the set of features that contributes most to the model

quality. The analysis proved that the highest explanatory power and the lower error come from the

combination of the house-specific features and the city dummies. The optimized versions of these

machine learning algorithms are forecasting based on this feature set, in order to evaluate their

performance and extract the feature importance. The most improved model was the Tuned Random

Forest Regressor but with quite similar performance metrics to the Tuned Support Vector

Regression. The results of the analysis show that the most important features in the forecasting

procedure are the number of bathrooms, the size of the rooms and the parking spaces. Additionally,

beta coefficients imply that properties located on the top floors have a considerable higher rental

price, while properties situated in Porto Alegre or in Campinas face a negative impact on rental

prices due to their location.

Item Type: Dissertation (University of Nottingham only)
Date Deposited: 25 Apr 2023 11:53
Last Modified: 25 Apr 2023 11:53

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