16:00 - 16:30, 17th Mar 2021

Property (Big) Data towards the Accomplishment of Housing Nations and SDG-2030 Goals 4, 8, 9, 11 and 17

Daniele Gambero
Session Description
How the use of (Big) Data could help countries in Housing their Population (resolving the affordable housing dilemma) and contemporaneously working on a positive achievement of the SDG-2030: 1. Goal 4 “Quality education which doesn’t mean necessarily higher education” 2. Goal 8 “Decent Work and Economic Growth through a diversified aim of developers in building sustainable communities more than purely buildings” 3. Goal 9 “Township are economic growth booster if planned with a 360 degrees eye covering housing, commercial and industrial areas or transforming each township in a thriving centre of economic growth” 4. Goal 11 “ Sustainable cities and communities can only be built one township at the time, creating connectivity, mobility and attracting people because of economic drivers. In other words, by targeting the three goals above the result will be automatic achievement ion Goal 11” 5. Goal 17 “Restoring earth-health is the humankind challenge of this just started decade task and only through partnership it can be achieved. Big dreams can be transformed in fact, Proptech could be leading the way in resolving some of the multiple challenges faced when evolving an industry from “brick & mortar” to a full adoption of digitalisation. Digitisation doesn’t eliminate jobs, on the contrary is contributing to create totally different skills’ set while helping people to learn them. Adopting and committing ourselves toward a full accomplishment of SDG-2030 would be the only way to move towards a better world! Guaranteeing a roof for everyone, decent work and economic growth, open education for everyone are a must which can only be accomplished when data are through fully analysed and use in a formulating reliable predictive analysis for housing. Delivering enough affordable homes is not only matter of constructing them! The deliverables have to be in the right place, quantity, sizes and pricing! I will introduce a simple but very effective modelling based on different types of data set which could help in the above.

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