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- Tasks: Lead city-level real estate forecasts and communicate insights to clients.
- Company: Join Oxford Economics, a top global provider of economic research and consulting.
- Benefits: Enjoy a full-time role in London with opportunities for remote work and professional growth.
- Why this job: Be part of an innovative team shaping the future of real estate economics.
- Qualifications: 7+ years in real estate forecasting; degree in real estate, economics, or data science preferred.
- Other info: Diversity and inclusion are core values at Oxford Economics.
The predicted salary is between 43200 - 72000 £ per year.
Oxford Economics is a leading global provider of economic forecasts, research, and consulting solutions. You will be a leading member of the team responsible for producing forecasts and research for the global city-level real estate market, a new product in the company. As a senior member of the team, you will be responsible for delivering global city-level real estate forecasts in co-ordination with the regional real estate economics leads and the director of global real estate economics.
In addition, you will independently develop research ideas, take responsibility for authoring content, and present our views to clients. You have a passion for dealing with large datasets, forecasting models, and a willingness to continuously improve productivity by utilising modern technology and data science approaches. You will have a keen eye for detail and a rigorous approach to ensuring quality. You will also have a desire to communicate our global city-level real estate views to clients whether that’s written reports, chartbooks, presentations, webinars or other mediums. This will help build strong client relationships over time.
Key Responsibilities- Deliver global city-level real estate forecasts in co-ordination with the rest of the real estate economics team
- Communicate our global city-level real estate views to clients (writing, analytics, presenting)
- Manage forecasting and data processes in the real estate economics team
- Make continuous productivity enhancements and, in time, help to develop new products and services
- More than seven years’ experience delivering real estate forecasts and research
- Strong track record in dealing with large datasets, co-ordinating complex processes, successful teamworking, and communicating views to clients
- Excellent academic credentials with a degree in real estate, economics or data science preferred
Oxford Economics is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.
Associate Director - Real Estate Economics (UK) employer: Oxford Economics
Contact Detail:
Oxford Economics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Associate Director - Real Estate Economics (UK)
✨Tip Number 1
Familiarise yourself with the latest trends in global city-level real estate economics. This will not only help you understand the market better but also allow you to engage in informed discussions during interviews.
✨Tip Number 2
Network with professionals in the real estate and economics sectors. Attend industry events or webinars to connect with potential colleagues and learn about the latest developments, which can give you an edge in your application.
✨Tip Number 3
Brush up on your data analysis skills, especially with large datasets. Being able to demonstrate your proficiency in forecasting models and data science approaches will be crucial for this role.
✨Tip Number 4
Prepare to showcase your communication skills. Since the role involves presenting complex information to clients, practice articulating your thoughts clearly and confidently, whether through mock presentations or discussions with peers.
We think you need these skills to ace Associate Director - Real Estate Economics (UK)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in real estate forecasting and research. Emphasise your ability to handle large datasets and your familiarity with forecasting models, as these are crucial for the role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for real estate economics and your desire to contribute to Oxford Economics. Mention specific examples of your past work that align with the responsibilities outlined in the job description.
Showcase Communication Skills: Since the role involves presenting views to clients, include examples of how you've effectively communicated complex data or forecasts in previous roles. This could be through reports, presentations, or webinars.
Highlight Continuous Improvement: Discuss any experiences where you have implemented productivity enhancements or utilised modern technology and data science approaches in your work. This will demonstrate your commitment to improving processes within the team.
How to prepare for a job interview at Oxford Economics
✨Showcase Your Data Skills
Since the role involves dealing with large datasets and forecasting models, be prepared to discuss your experience with data analysis. Bring examples of how you've used data to drive decisions or improve processes in previous roles.
✨Communicate Clearly
You'll need to present complex information to clients, so practice explaining your ideas clearly and concisely. Consider preparing a short presentation on a relevant topic to demonstrate your communication skills during the interview.
✨Demonstrate Team Collaboration
This position requires strong teamwork, so be ready to share examples of successful collaborations. Highlight how you’ve worked with others to achieve common goals, especially in high-pressure situations.
✨Prepare for Technical Questions
Expect questions that test your knowledge of real estate economics and forecasting techniques. Brush up on key concepts and be ready to discuss how you would approach specific forecasting challenges.
