Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach
Often, prospective tenants need to know the rental price of an apartment, and homeowners need to know how best to price their apartments. This work aims to predict house rental prices in Lagos, Nigeria, using machine learning by examining the relationship between the rental price and features such...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2024-08-01
|
| Series: | ABUAD Journal of Engineering Research and Development |
| Subjects: | |
| Online Access: | https://journals.abuad.edu.ng/index.php/ajerd/article/view/696 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850052073067905024 |
|---|---|
| author | Sunday Oluyele Juwon Akingbade Victor Akinode Royal Idoghor |
| author_facet | Sunday Oluyele Juwon Akingbade Victor Akinode Royal Idoghor |
| author_sort | Sunday Oluyele |
| collection | DOAJ |
| description |
Often, prospective tenants need to know the rental price of an apartment, and homeowners need to know how best to price their apartments. This work aims to predict house rental prices in Lagos, Nigeria, using machine learning by examining the relationship between the rental price and features such as the number of bedrooms, bathrooms, toilets, location and house status(newly built, furnished, and/or serviced). Five machine learning models were trained and evaluated using mean absolute error (MAE), root mean squared error (RMSE) and r-square (R2); the random forest regression model outperformed the other four models with the lowest MAE, RMSE and the highest R2. This study also revealed that the number of bedrooms and the apartment's location are the most significant predictors, confirmed using the feature importance analysis. The developed model can be used to estimate the rental price of a property in Lagos, Nigeria.
|
| format | Article |
| id | doaj-art-e4671776fd204af0b50ecb7eb6a435f7 |
| institution | DOAJ |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-e4671776fd204af0b50ecb7eb6a435f72025-08-20T02:52:56ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852024-08-017210.53982/ajerd.2024.0702.21-j582Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning ApproachSunday Oluyele0Juwon Akingbade1Victor Akinode2Royal Idoghor3Department of Computer Engineering, Federal University Oye Ekiti, Oye, Ekiti State, NigeriaDepartment of Computer Engineering, Federal University Oye Ekiti, Oye, Ekiti State, NigeriaDepartment of Computer Engineering, Federal University Oye Ekiti, Oye, Ekiti State, NigeriaDepartment of Computer Engineering, Federal University Oye Ekiti, Oye, Ekiti State, Nigeria Often, prospective tenants need to know the rental price of an apartment, and homeowners need to know how best to price their apartments. This work aims to predict house rental prices in Lagos, Nigeria, using machine learning by examining the relationship between the rental price and features such as the number of bedrooms, bathrooms, toilets, location and house status(newly built, furnished, and/or serviced). Five machine learning models were trained and evaluated using mean absolute error (MAE), root mean squared error (RMSE) and r-square (R2); the random forest regression model outperformed the other four models with the lowest MAE, RMSE and the highest R2. This study also revealed that the number of bedrooms and the apartment's location are the most significant predictors, confirmed using the feature importance analysis. The developed model can be used to estimate the rental price of a property in Lagos, Nigeria. https://journals.abuad.edu.ng/index.php/ajerd/article/view/696Machine LearningPrice PredictionReal EstateRegressionRandom Forest |
| spellingShingle | Sunday Oluyele Juwon Akingbade Victor Akinode Royal Idoghor Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach ABUAD Journal of Engineering Research and Development Machine Learning Price Prediction Real Estate Regression Random Forest |
| title | Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach |
| title_full | Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach |
| title_fullStr | Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach |
| title_full_unstemmed | Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach |
| title_short | Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach |
| title_sort | prediction of urban house rental prices in lagos nigeria a machine learning approach |
| topic | Machine Learning Price Prediction Real Estate Regression Random Forest |
| url | https://journals.abuad.edu.ng/index.php/ajerd/article/view/696 |
| work_keys_str_mv | AT sundayoluyele predictionofurbanhouserentalpricesinlagosnigeriaamachinelearningapproach AT juwonakingbade predictionofurbanhouserentalpricesinlagosnigeriaamachinelearningapproach AT victorakinode predictionofurbanhouserentalpricesinlagosnigeriaamachinelearningapproach AT royalidoghor predictionofurbanhouserentalpricesinlagosnigeriaamachinelearningapproach |