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CoStar Group

Commercial real estate data, analytics, marketplaces

Econometric Forecasting Lead

Data ScienceConfirmed live in the last 24 hours
Full-timeRichmond, VA, USA

About the Job

Requirements

  • A master's degree in econometrics, statistics, data science, or a related field from an accredited, not-for-profit, in-person college or university.
  • A track record of commitment to prior employers.
  • 5–7+ years of relevant experience in econometric modeling, quantitative analytics, or commercial real estate forecasting.
  • Strong proficiency in SQL (SSMS/Databricks), R, and Python for data manipulation, analysis, and modeling.
  • Expertise in econometric models, including regression analysis, time series forecasting, and advanced statistical techniques.
  • Hands-on experience working with large data sets.
  • Proven experience in developing and refining forecasting models.
  • Strong understanding of commercial real estate dynamics, market drivers, and forecasting challenges.
  • Exceptional communication skills and the ability to explain complex models to non-technical stakeholders.
  • High organization and attention to detail.
  • A proactive, self-driven approach and initiative to identify and implement improvements.

Responsibilities

  • Monitor and track forecast accuracy using defined metrics such as RMSE and MAE, provide regular reports on accuracy and back testing, and serve up data points used.
  • Work with the analytics team to provide data for dashboards tracking forecast accuracy and make insights actionable for analysts, department leaders, and executives.
  • Explore new economic inputs and refine existing variables and coefficients to enhance the precision and accuracy of existing forecasting models.
  • Conduct independent quantitative research to evaluate, track, and improve commercial real estate models, data series, and estimations.
  • Lead and refine the quarterly forecast-accuracy review process, incorporating feedback from analysts and external factors to improve models.
  • Develop and innovate forecast models for specialty real estate sectors, nowcasts, space-level analytics, and emerging real estate markets.
  • Maintain clear documentation on forecast models and methodologies, including explanations of inputs and variables.
  • Provide ongoing education to the analytics team on forecast updates, drivers, and accuracy.
  • Lead communication of forecast processes, guidelines, results, and updates to external customers, including back testing and accuracy reviews, and assist customers with model validations.

Desired Qualifications

  • An advanced degree (master's or PhD) in econometrics, statistics, data science, or a related field from an accredited, not-for-profit, in-person college or university.
  • Experience with Python-based common data science tools such as Jupyter Notebooks, NumPy, Pandas, and scikit-learn.
  • Experience writing clean, reproducible, robust, and scalable analysis code.
  • Familiarity with cloud platforms such as AWS and Azure, and data migration processes.
  • Experience in space-level analytics or granular building data modeling.
  • Proficiency in Stata.

About the Company

CoStar Group

Commercial real estate data, analytics, marketplaces

CoStar Group provides information, analytics, and online marketplaces for the commercial real estate industry. Its core products are detailed property data, market analyses, and tools to compare and evaluate properties, delivered via subscription access. It also runs online marketplaces like Apartments.com and LoopNet that connect renters with apartments and buyers/tenants with commercial properties, earning revenue from subscriptions, advertising, and transaction fees. The company differentiates itself through a large, integrated data platform and a suite of marketplaces that cover both residential rentals and commercial properties, with a strong presence in the U.S. and expanding globally. Its goal is to help brokers, property owners, investors, and lenders make informed real estate decisions and efficiently connect buyers, renters, and property listings across markets.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Washington DC, District of Columbia
Founded
1987

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