Predicting residential demand: applying random forest to predict housing demand in Cape Town
| dc.contributor.advisor | McGaffin, Robert | |
| dc.contributor.advisor | Nyirenda, Juwa Chiza | |
| dc.contributor.author | Dyer, Ross | |
| dc.date.accessioned | 2019-02-18T10:34:03Z | |
| dc.date.available | 2019-02-18T10:34:03Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-02-18T08:40:56Z | |
| dc.description.abstract | The literature shows that Random Forest is a suitable technique to predict a target variable for a household with completely unseen characteristics. The models produced in this paper show that the characteristics of a household can be used to predict the Type of Dwelling, the Tenure and the Number of Bedrooms to varying degrees of accuracy. While none of the sets of models produced indicate a high degree of predictive accuracy relative to hurdle rates, the paper does demonstrate the value that the Random Forest technique offers in moving closer to an understanding of the complex nature of housing demand. A key finding is that the Census variables available for the models are not discriminatory enough to enable the high degree of accuracy expected from a predictive model. | |
| dc.identifier.apacitation | Dyer, R. (2018). <i>Predicting residential demand: applying random forest to predict housing demand in Cape Town</i>. (). University of Cape Town ,Engineering and the Built Environment ,Department of Construction Economics and Management. Retrieved from http://hdl.handle.net/11427/29602 | en_ZA |
| dc.identifier.chicagocitation | Dyer, Ross. <i>"Predicting residential demand: applying random forest to predict housing demand in Cape Town."</i> ., University of Cape Town ,Engineering and the Built Environment ,Department of Construction Economics and Management, 2018. http://hdl.handle.net/11427/29602 | en_ZA |
| dc.identifier.citation | Dyer, R. 2018. Predicting residential demand: applying random forest to predict housing demand in Cape Town. University of Cape Town. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Dyer, Ross AB - The literature shows that Random Forest is a suitable technique to predict a target variable for a household with completely unseen characteristics. The models produced in this paper show that the characteristics of a household can be used to predict the Type of Dwelling, the Tenure and the Number of Bedrooms to varying degrees of accuracy. While none of the sets of models produced indicate a high degree of predictive accuracy relative to hurdle rates, the paper does demonstrate the value that the Random Forest technique offers in moving closer to an understanding of the complex nature of housing demand. A key finding is that the Census variables available for the models are not discriminatory enough to enable the high degree of accuracy expected from a predictive model. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Predicting residential demand: applying random forest to predict housing demand in Cape Town TI - Predicting residential demand: applying random forest to predict housing demand in Cape Town UR - http://hdl.handle.net/11427/29602 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/29602 | |
| dc.identifier.vancouvercitation | Dyer R. Predicting residential demand: applying random forest to predict housing demand in Cape Town. []. University of Cape Town ,Engineering and the Built Environment ,Department of Construction Economics and Management, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/29602 | en_ZA |
| dc.language.iso | eng | |
| dc.publisher.department | Department of Construction Economics and Management | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject.other | Property Studies | |
| dc.title | Predicting residential demand: applying random forest to predict housing demand in Cape Town | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |