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Predicting residential demand: applying random forest to predict housing demand in Cape Town
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 o...
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| Format: | Thesis |
| Language: | English |
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Department of Construction Economics and Management
2019
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| _version_ | 1867613249553301504 |
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| access_status_str | Open Access |
| author | Dyer, Ross |
| author2 | McGaffin, Robert |
| author_browse | Dyer, Ross McGaffin, Robert |
| author_facet | McGaffin, Robert Dyer, Ross |
| author_sort | Dyer, Ross |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/29602 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:08.525Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Department of Construction Economics and Management |
| publisherStr | Department of Construction Economics and Management |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/29602 Predicting residential demand: applying random forest to predict housing demand in Cape Town Dyer, Ross McGaffin, Robert Nyirenda, Juwa Chiza Property Studies 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. 2019-02-18T10:34:03Z 2019-02-18T10:34:03Z 2018 2019-02-18T08:40:56Z Master Thesis Masters MSc http://hdl.handle.net/11427/29602 eng application/pdf Department of Construction Economics and Management Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Property Studies Dyer, Ross Predicting residential demand: applying random forest to predict housing demand in Cape Town |
| thesis_degree_str | Master's |
| title | Predicting residential demand: applying random forest to predict housing demand in Cape Town |
| title_full | Predicting residential demand: applying random forest to predict housing demand in Cape Town |
| title_fullStr | Predicting residential demand: applying random forest to predict housing demand in Cape Town |
| title_full_unstemmed | Predicting residential demand: applying random forest to predict housing demand in Cape Town |
| title_short | Predicting residential demand: applying random forest to predict housing demand in Cape Town |
| title_sort | predicting residential demand applying random forest to predict housing demand in cape town |
| topic | Property Studies |
| url | http://hdl.handle.net/11427/29602 |
| work_keys_str_mv | AT dyerross predictingresidentialdemandapplyingrandomforesttopredicthousingdemandincapetown |