Abstract
The housing segment is one of the most lucrative industries in almost all parts of the world, and with an emerging place like Dubai with global attraction the real-estate market is set to expand more. With this, there is an importance to be able to understand such markets with in-depth expertise which not only helps to be a subject matter expert, but also provide recommendations and insights to customers and stakeholders. According to Asteco, UAE would witness an addition of 38,500 apartments and 3,800 villas and Dubai is estimated to account the most with 30,000 flats and 3,500 villas in 2022. Abu Dhabi is expected to see around 2,000 residential units to be given to Reem Island, 2,000 each in Al Raha Beach and Yas Island and 1,200 in Saadiyat Island. In January 2022, more than 53% transactions were for ready properties and 47% for off-plan properties.(Frank, 2022)
There are different methods to segment properties, which is mainly dependent on collecting information about the apartment done through real estate agents or construction groups, who provide the information publicly or on request. With the evolution of the housing market, evidently due to the expansion of population and other development scope in different regions, the need to develop effective marketing strategies with high quality content as well as relevance has become key to sustenance. It becomes a starting point to building relationships with consumers, and this can be done by marketing the appropriate property to the right customer groups through online email marketing, brochures as well as pamphlets.
In this report, we plan to use commonly available datasets, which are basically Dubai property records collected. We plan to implement an unsupervised clustering technique on the data to segment apartments/properties based on different traits. Even before that, we would be going through the details of the dataset through some exploratory data analyses, and then cleaning the data for inconsistency and then finally clustering the apartment ids based on different traits.
Publication Date
12-2022
Document Type
Master's Project
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Advisor
Sanjay Modak
Advisor/Committee Member
Ioannis Karamitsos
Recommended Citation
Ahli, Hamad, "House Price Classification using Clustering Algorithms" (2022). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11381
Campus
RIT Dubai
