dc.contributor.author | Chemutai, Merab | |
dc.date.accessioned | 2021-05-07T12:35:50Z | |
dc.date.available | 2021-05-07T12:35:50Z | |
dc.date.issued | 2020-12-18 | |
dc.identifier.citation | Chemutai, M. (2020). Developing a predictive property valuation model for residential property. Case study-Kawempe division. (Unpublished undergraduate dissertation) Makerere University. Kampala,Uganda | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12281/10602 | |
dc.description | A research project submitted to the Department of Construction Economics and Management
for the award of a Degree of Bachelor of Science in Land Economics of Makerere University. | en_US |
dc.description.abstract | The property market is one of the greatest evolving sectors in the universe apart from food
to a human life. These property market should meet the criteria of adequacy, affordability
and decent way of performing transactions. As these market evolves, in relation to
technology advancements, there is need for a proper way of keeping property values to
check on the growth and how to improve these sector especially for the planning and
growth of country.
The property market is interwoven with property valuations which is done for a number of
purposes specially to process for mortgage financing, taxation and compensation. In
Uganda, the property valuation system is still manualized and this system greatly has
loopholes in relation to the global advancements, it is impossible to verify all the
information provided by the valuers and at the same time, the process is lengthy and time
consuming and involves repeated works and the planning authorities are neither efficient
nor transparent. The research focused on developing a predictive model that not only stores
data values but also quickens the process of valuation in a simpler manner. This model was
subject to residential property and can be adopted to any type of property.
The study employed a case study of Kawempe division. Descriptive designs and modeling
techniques on a sample 120 properties that were selected by obtaining property values
carried in the last 5 years that were got from various valuation firms was used. This data
was modeled using a hybrid software called anaconda and tested using two algorithms that
is to say; Ridge Linear Regression and Multiple Linear Regression to see which one
performs best.
The study has revealed since the real estate sector has become one of the safe havens for
investors seeking meaningful returns, there is a great need for the valuation systems in
Uganda to adopt artificial intelligence as the economy evolves.
The researcher put out several recommendations that could be a solution to the continuous
growth of the property sector and as technology advances, machine learning is vital
because this technology focuses on the development of computer programs that can access
data and use it learn for themselves. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Makerere University | en_US |
dc.subject | Property valuation model | en_US |
dc.subject | Residential property | en_US |
dc.title | Developing a predictive property valuation model for residential property. Case study-Kawempe division | en_US |
dc.type | Thesis | en_US |