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https://scholarbank.nus.edu.sg/handle/10635/187133
DC Field | Value | |
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dc.title | THE IDEAL TENANT MIX FOR HOUGANG POINT SHOPPING CENTRE | |
dc.contributor.author | MAK MEI WOON | |
dc.date.accessioned | 2021-03-09T08:45:41Z | |
dc.date.available | 2021-03-09T08:45:41Z | |
dc.date.issued | 1999 | |
dc.identifier.citation | MAK MEI WOON (1999). THE IDEAL TENANT MIX FOR HOUGANG POINT SHOPPING CENTRE. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/187133 | |
dc.description.abstract | An effective and well-balanced tenant mix is a critical component in a shopping centre. It is not only the most vital, but also the hardest to obtain. It is highly dependable on the forecast of consumer demand for the products or services sold by tenants. Through balanced tenancy, the stores in a planned shopping centre complement each other in the quality and variety of their product offerings, and the kind and number of stores are linked to the overall needs of the surrounding population in the centre's catchment areas. In this dissertation, the author suggests a practical and cost-effective research method. A consumer preference weighting methodology based on three parameters, namely tenant category preference, tenant category ranking preference, and tenant shopping likelihood consolidated into a single composite tenant choice index was developed. The emphasis in this study is on describing the logic of the research methodology, using a real-life example of the upcoming Hougang Point Shopping Centre. An analysis on the demographics, shopping patterns and shoppers' preferences in the trade area is conducted. | |
dc.source | SDE BATCHLOAD 20210308 | |
dc.type | Thesis | |
dc.contributor.department | SCHOOL OF BUILDING & REAL ESTATE | |
dc.contributor.supervisor | SIM LOO LEE | |
dc.description.degree | Bachelor's | |
dc.description.degreeconferred | BACHELOR OF SCIENCE (REAL ESTATE) | |
Appears in Collections: | Bachelor's Theses |
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IdeMak.pdf | 41.95 MB | Adobe PDF | RESTRICTED | None | Log In |
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