The rSBI Certificate in Finance & Economics

The Rutgers Stackable Business Innovation (rSBI) Program

Concentrations and Courses

Below is a listing of concentrations and their courses, offered by the Department of Finance & Economics.

Note that all courses are not offered every semester.

Financial Data Analytics and FinTech

This concentration introduces students to the use of financial analytics used by finance practitioners. It provides a strong and rigorous introduction to the use of financial applications in fintech and machine learning.

New Technologies in Commercial Real Estate

This concentration serves two objectives. First, students will analyze investment opportunities in commercial property markets using market, property, and lease information. Their analysis will be aided by using leading-edge tools and technology such as CoStar and ARGUS. The second objective involves an in-depth study of real estate market analysis followed by a use of these tools, combined with a basic understanding of real estate law and negotiation, to analyze and value development opportunities.

Concentration Course Descriptions

The courses below are specific to the Finance & Economics concentrations listed above; however, other courses are available within other master's level programs that students can take through the rSBI program.

Market Analysis and Valuation in Real Estate

This course explores the sources of property information and market data used in studies of real estate markets and provides an in-depth analysis of trends, market activity, sales, lending, and leasing. The course includes analysis of both residential and commercial real estate and covers demographic analysis, regional growth, construction cycles, urban land markets, and location theory. Exercises and applications focus on estimating and predicting property demand, supply, vacancy, and value using modeling in economics, statistical machine learning, and agent-based machine learning.

  • Course Number: 22:851:630
  • Prerequisites: Real Estate Finance (22:390:695)
  • Credits: 3
  • Delivery Mode: In-person
  • Programs Potentially Accepting Credit Transfer: MBA

Real Estate Development

This course overviews real estate development of urban places, including the many challenges of the development process such as analyzing market sectors and development opportunities, comprehending the development context of regulation, public policy and politics, raising investment capital, assembling land, program formulation, building types, construction management, marketing, and sales. Examples of development projects will be presented, each focusing on specific aspects of the process. Students will learn how to access and harvest online information to understand environmental and legal challenges to real estate redevelopment.

 

  • Course Number: 22:851:632
  • Prerequisites: Real Estate Finance (22:390:695) and Real Estate Law (22:851:650)
  • Credits: 3
  • Delivery Mode: In-person
  • Programs Potentially Accepting Credit Transfer: MBA

Blockchain and Cryptocurrency

Assuming the students have no prior knowledge in the cryptocurrency and blockchain space, it is important to introduce basic concepts and an overview of the blockchain landscape. Furthermore, the course will explain blockchain and crypto market microstructure concepts, and then we will introduce students to different data sources of both blockchain data and crypto market data. As a blockchain data scientist or a cryptocurrency analyst, you would need to analyze data and understand the present and future value and risk of the blockchain project and/or the cryptocurrency, similar to analyzing any company or financial instrument.

  • Course Number: 22:839:635
  • Prerequisites: Knowledge of Python
  • Credits: 3
  • Delivery Mode: In-Person, Online
  • Programs Potentially Accepting Credit Transfer: MS in Quantitative Finance, MBA

Machine Learning in Finance and Economics

The course has three parts. The first part introduces fundamentals and traditional machine learning techniques including cross validation, regularization, regression trees, ensemble methods, random forests, and gradient boosting. Python libraries scikit-learn (‘sklearn’) and XGBoost will be used. The second part will provide an introduction to Deep Learning. Instead of treating deep neural networks as just another powerful algorithms, we will emphasize what they make possible in financial applications that are difficult or impossible to achieve with earlier methods. Keras and Tensorflow will be used. Cloud computing will also be introduced to facilitate data management and training of these models. The third part of the course is more experiential. Small student teams will work on projects to apply the techniques covered in the course. Projects will use real data and attempt to solve real problems faced in financial industry. The students will have flexibility to choose their topic based on their interest. Applications may focus on asset return predictions, credit risk, mergers, and real estate values among others.

  • Course Number: 22:839:636
  • Prerequisites: 22:390:603 or 22:839:603 Investment Analysis AND Ability to write non-trivial code in Matlab and Python
  • Credits: 3
  • Delivery Mode: In Person
  • Programs Potentially Accepting Credit Transfer: MS in Quantitative Finance, MBA

Financial Forecasting and Simulation

Forecasts of financial variables play a prominent role in financial and business decision-making. This course provides an overview of modern statistical and econometric methods for predicting financial variables and evaluating forecasts. Students will develop an understanding of the basic components of a forecasting model, how to build their own forecasting models, and how to evaluate the performance of forecasting models. We emphasize intuitive understanding of the basic concepts and techniques and practical applications to real-world data. Topics covered include linear projections; modeling and forecasting trend, seasonality and cycles; AR, MA, ARMA, ARIMA, and VAR models; forecasting with fundamentals; conditional forecasting models and scenarios analysis (stress testing); evaluating and combining forecasts; unit roots, cointegration and stochastic trends; smoothing and shrinkage; ARCH, GARCH and volatility forecast; unobserved components models and Kalman filter forecasting; data snooping, bootstrap, and reality check.

  • Course Number: 22:839:637
  • Prerequisites: Ability to write non-trivial code in Matlab and Python. Basic calculus, linear algebra, probability and statistics, and econometrics.
  • Credits: 3
  • Delivery Mode: In Person
  • Programs Potentially Accepting Credit Transfer: MS in Quantitative Finance, MBA

Real Estate Law

This course provides an overview of the legal issues which confront the real estate executive from the commencement of a real estate transaction and throughout the relationship between the parties to such transactions. In addition to many standard real property law concepts will be covered, the course will focus on the transactional aspects of the real estate business, including acquisition, disposition, development, investment, management, leasing, tax implications and negotiations. The course will further emphasize the challenges new technologies influence legal processes as employed in property markets.

  • Course Number: 22:851:650
  • Prerequisites: None
  • Credits: 3
  • Delivery Mode: In-person
  • Programs Potentially Accepting Credit Transfer: MBA

Real Estate Finance

The central objective of this course is to provide students with the background and tools necessary to analyze property markets from the perspective of an institutional investor. This involves acquiring and using market and property-related information to develop projections of the expected future cash flows generated by a given property and using them to construct measures of value, risk, and return. The impact of new lending and leasing platforms on property markets will also be considered. The course provides extensive training and certification in ARGUS, a real estate industry-specific program used for entering and compiling market, property, and lease information.

  • Course Number: 22:390:695
  • Prerequisites: Ability to understand and apply discounted cash flow analysis.
  • Credits: 3
  • Delivery Mode: In-person
  • Programs Potentially Accepting Credit Transfer: MBA

Enrollment to Open 2021- Request Information

Enrollment for The Rutgers Stackable Business Innovation Program (rSBI) will begin in 2021, however; you can start taking a course today to get exposure to the high-quality content this program has to offer. To express interest in the Rutgers Stackable Business Innovation Program (rSBI) or enroll in a course as a non-matriculated student, please complete the form below and we will contact you. Courses would be counted toward certificates when the program is launched, and students are admitted to it.

 

Program Manager
Luke Greeley

Program Director
Distinguished Professor Benjamin Melamed

By submitting this form, you agree to receive emails, text messages, telephone calls, and prerecorded messages from Rutgers Business School regarding educational programs. You understand that such calls, emails, and messages may be sent using automated technology. You may opt out at any time. Please view our Privacy Policy or Contact Us for more details.