Predictive Modelling for Residential Construction Demands Using ElasticNet Regression
The residential construction sector is critical to economic stability and housing availability. Residential construction demands often fluctuate due to demographic, economic, social, or market condition variables. This study seeks to investigate the significance of these external variables and produ...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/10/1649 |
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| author | Elrasheid Elkhidir Tirth Patel James Olabode Bamidele Rotimi |
| author_facet | Elrasheid Elkhidir Tirth Patel James Olabode Bamidele Rotimi |
| author_sort | Elrasheid Elkhidir |
| collection | DOAJ |
| description | The residential construction sector is critical to economic stability and housing availability. Residential construction demands often fluctuate due to demographic, economic, social, or market condition variables. This study seeks to investigate the significance of these external variables and produce a predictive model for residential construction demand using ElasticNet regression. Adopting New Zealand as a case study and leveraging datasets from Statistics New Zealand, this research identifies key demographic, economic, and market factors influencing four building categories: retirement villages, apartments, multiunit developments, and standalone houses. The research results indicate that age groups, particularly the 20−39 and 65+ age groups, and economic indicators, such as the house price index and unemployment rates, have high prediction powers. The models showed high accuracy for some categories, with R<sup>2</sup> values exceeding 0.87 for retirement villages and 0.91 for multi-units. Challenges were encountered with standalone houses and apartments due to residual variance. The research findings highlight the importance of targeted urban planning and policy adjustments to satisfy the requirements of specific age groups, address housing affordability and demographic shifts, and cater to prevailing market conditions. This research provides practical insights and guidance for urban planners, public housing agencies, residential developers, and residential contractors while offering a robust methodological framework for predictive modelling in the construction sector. |
| format | Article |
| id | doaj-art-18a46cf8cd094a3c9a2771497f528eb4 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-18a46cf8cd094a3c9a2771497f528eb42025-08-20T02:33:31ZengMDPI AGBuildings2075-53092025-05-011510164910.3390/buildings15101649Predictive Modelling for Residential Construction Demands Using ElasticNet RegressionElrasheid Elkhidir0Tirth Patel1James Olabode Bamidele Rotimi2School of Built Environment, Massey University, Auckland 0632, New ZealandDepartment of Civil and Natural Resources Engineering, University of Canterbury, Christchurch 8041, New ZealandSchool of Built Environment, Massey University, Auckland 0632, New ZealandThe residential construction sector is critical to economic stability and housing availability. Residential construction demands often fluctuate due to demographic, economic, social, or market condition variables. This study seeks to investigate the significance of these external variables and produce a predictive model for residential construction demand using ElasticNet regression. Adopting New Zealand as a case study and leveraging datasets from Statistics New Zealand, this research identifies key demographic, economic, and market factors influencing four building categories: retirement villages, apartments, multiunit developments, and standalone houses. The research results indicate that age groups, particularly the 20−39 and 65+ age groups, and economic indicators, such as the house price index and unemployment rates, have high prediction powers. The models showed high accuracy for some categories, with R<sup>2</sup> values exceeding 0.87 for retirement villages and 0.91 for multi-units. Challenges were encountered with standalone houses and apartments due to residual variance. The research findings highlight the importance of targeted urban planning and policy adjustments to satisfy the requirements of specific age groups, address housing affordability and demographic shifts, and cater to prevailing market conditions. This research provides practical insights and guidance for urban planners, public housing agencies, residential developers, and residential contractors while offering a robust methodological framework for predictive modelling in the construction sector.https://www.mdpi.com/2075-5309/15/10/1649residential demanddemand forecastingdemand projectionsconstruction demandresidential forecasting |
| spellingShingle | Elrasheid Elkhidir Tirth Patel James Olabode Bamidele Rotimi Predictive Modelling for Residential Construction Demands Using ElasticNet Regression Buildings residential demand demand forecasting demand projections construction demand residential forecasting |
| title | Predictive Modelling for Residential Construction Demands Using ElasticNet Regression |
| title_full | Predictive Modelling for Residential Construction Demands Using ElasticNet Regression |
| title_fullStr | Predictive Modelling for Residential Construction Demands Using ElasticNet Regression |
| title_full_unstemmed | Predictive Modelling for Residential Construction Demands Using ElasticNet Regression |
| title_short | Predictive Modelling for Residential Construction Demands Using ElasticNet Regression |
| title_sort | predictive modelling for residential construction demands using elasticnet regression |
| topic | residential demand demand forecasting demand projections construction demand residential forecasting |
| url | https://www.mdpi.com/2075-5309/15/10/1649 |
| work_keys_str_mv | AT elrasheidelkhidir predictivemodellingforresidentialconstructiondemandsusingelasticnetregression AT tirthpatel predictivemodellingforresidentialconstructiondemandsusingelasticnetregression AT jamesolabodebamidelerotimi predictivemodellingforresidentialconstructiondemandsusingelasticnetregression |