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Remote Real Estate Data Analyst Jobs (NOW HIRING)

LEO&Co

Part Time Data Analyst (Remote)

New York, NY · Remote

A real estate services firm in New York City has a great Remote opportunity awaiting a new Data Analyst. In this role, the Data Analyst (Remote) will be responsible for ensuring the business makes ...

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Remote Real Estate Data Analyst information

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How much do remote real estate data analyst jobs pay per year?

As of Aug 9, 2026, the average yearly pay for remote real estate data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What is the difference between Remote Real Estate Data Analyst vs Remote Real Estate Market Research Analyst?

AspectRemote Real Estate Data AnalystRemote Real Estate Market Research Analyst
Required CredentialsBachelor's in Data Science, Real Estate, or related field; proficiency in data analysis toolsBachelor's in Economics, Real Estate, or related; strong research and analytical skills
Work EnvironmentRemote, often collaborating with real estate firms and data providersRemote, working with market reports, surveys, and industry data
Employer & Industry UsageReal estate agencies, investment firms, data companiesReal estate consultancies, market research firms, brokerage firms

The Remote Real Estate Data Analyst focuses on analyzing real estate data to support investment decisions, while the Remote Real Estate Market Research Analyst emphasizes studying market trends and consumer insights. Both roles are remote, require analytical skills, and serve the real estate industry, but they differ in their primary focus and types of data handled.

What is a remote real estate data analyst?

A Remote Real Estate Data Analyst is a professional who works from a remote location to collect, process, and analyze data related to real estate markets. Their responsibilities often include interpreting trends, evaluating property values, and providing insights to support buying, selling, and investment decisions. They use statistical tools and software to analyze data such as market trends, demographic information, and property performance. By working remotely, they leverage digital platforms to access and share data with real estate companies, investors, and other stakeholders. This role is ideal for those who are analytical, detail-oriented, and comfortable working independently with large datasets.

How does a remote real estate data analyst typically collaborate with other team members?

As a Remote Real Estate Data Analyst, collaboration with colleagues—such as real estate agents, market researchers, and business development teams—mainly occurs via digital tools like video conferencing, cloud-based data platforms, and instant messaging. You’ll regularly attend virtual meetings to present data insights, discuss market trends, and support decision-making processes. Clear communication and proactive sharing of analyses are key to ensuring your data-driven recommendations align with overall business goals, despite working from different locations.

What are the key skills and qualifications needed to thrive as a remote real estate data analyst?

To thrive as a Remote Real Estate Data Analyst, you need a strong background in data analysis, real estate market knowledge, and proficiency with statistical methods, often supported by a degree in finance, economics, or a related field. Familiarity with data visualization tools (like Tableau or Power BI), advanced Excel, SQL, and sometimes Python or R is typically required. Exceptional attention to detail, critical thinking, and effective remote communication are vital soft skills for this position. These skills enable analysts to deliver accurate insights, support data-driven decision-making, and collaborate efficiently with remote teams in a dynamic real estate environment.
More about Remote Real Estate Data Analyst jobs
What cities are hiring for Remote Real Estate Data Analyst jobs? Cities with the most Remote Real Estate Data Analyst job openings:
What are the most commonly searched types of Real Estate Data Analyst jobs? The most popular types of Real Estate Data Analyst jobs are:
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Remote Real Estate Data Analyst jobs near you
Infographic showing various Remote Real Estate Data Analyst job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $82,640 per year, or $39.7 per hour.

Real Estate Data Scientist - Remote

Harbor Freight Tools

Calabasas, CA • On-site, Remote

$98K - $147K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 13 days ago

Job description

The Real Estate Data Scientist is responsible for developing advanced analytical models and data-driven tools that support strategic real estate decisions across the organization. This role partners closely with Real Estate, Finance, Marketing, and Supply Chain teams to deliver predictive insights related to site selection, network optimization, sales forecasting, and market planning. This role incorporates advanced spatial modeling, geostatistics, and geospatial data engineering to evaluate trade areas, quantify market potential, and optimize network performance.
This position combines strong statistical modeling, data engineering, and business acumen to translate complex data into actionable recommendations. The Real Estate Data Scientist plays a critical role in advancing the organization's use of machine learning, automation, and predictive analytics to improve decision quality and scalability. This is a senior individual contributor role with no direct people management responsibility.
Duties and Responsibilities
  • Advanced Analytics & Predictive Modeling
    • Develop and deploy predictive models for site selection, sales forecasting, cannibalization, and market potential.
    • Build and maintain machine learning models using regression, classification, clustering, optimization, and spatial modeling techniques.
    • Apply spatial statistical methods (e.g., spatial regression, geographically weighted regression, spatial autocorrelation) to capture geographic variation in demand drivers.
    • Develop trade area and customer draw models (e.g., Huff/gravity models) to estimate market share and competitive impact.
    • Incorporate spatial features such as proximity, co-tenancy, demographics, traffic patterns, and nearby store performance into predictive models.
    • Design methodologies for forecasting store performance under various scenarios, including spatial and competitive effects.
    • Continuously monitor and improve model performance and accuracy. 
  • Data Engineering & Automation
    • Design scalable data pipelines integrating real estate, customer, demographic, sales, and geospatial datasets (parcel, census, traffic, mobility, POI data).
    • Perform geospatial data processing including geocoding, spatial joins, coordinate transformations, and spatial indexing (e.g., H3 or similar frameworks).
    • Write efficient SQL and Python workflows to automate recurring analyses, spatial feature engineering, and model refreshes.
    • Ensure data quality, consistency, and reproducibility across analytical outputs, including alignment of spatial boundaries and geographic hierarchies.
  • Real Estate Strategy & Decision Support
    • Partner with Real Estate teams to support site selection, market entry, relocations, and closures.
    • Develop drive-time and network-based trade area analyses to assess accessibility and market reach.
    • Conduct market coverage and white space analysis to identify expansion opportunities and underserved areas.
    • Build location-allocation and network optimization models to determine optimal site placement.
    • Quantify cannibalization and competitive effects using spatial overlap and proximity-based modeling.
    • Provide quantitative insights for Real Estate Committee (REC) evaluations and executive decisions.
    • Develop scoring frameworks and decision tools to prioritize opportunities.         
  • Visualization & Communication
    • Create clear, compelling visualizations and dashboards (Tableau, Power BI, or similar) to communicate insights.
    • Develop interactive geospatial visualizations including trade area maps, performance heatmaps, and market opportunity analyses.
    • Present analytical findings and recommendations to senior leadership and non-technical stakeholders.
  • Experimentation & Innovation
    • Design and execute experiments (A/B tests, quasi-experimental designs) to evaluate real estate strategies.
    • Implement geo-based testing frameworks (e.g., test vs. control markets) to measure impact of site decisions.
    • Apply causal inference methods (e.g., difference-in-differences, synthetic control) accounting for geographic spillovers.
    • Explore new data sources (e.g., mobility, foot traffic) and modeling techniques to enhance predictive capabilities.
    • Contribute to building a best-in-class real estate analytics capability.
  • Cross-Functional Collaboration
    • Work closely with GIS, Data Engineering, Finance, Marketing, and IT teams to align data and models.
    • Partner with GIS teams to ensure alignment between spatial analysis, mapping, and production data pipelines.
    • Translate business problems into analytical solutions and actionable insights.
Scope
  • Staff supervision and development:  No
  • Decision making: 
    • Develops models and analytical frameworks used in strategic decision-making
  • Travel:  Up to 10%
  • Flex Designation:  Anywhere

The anticipated salary range for this position is $98,500-$147,800 depending on location, knowledge, skills, education and experience. This position is also eligible for an annual discretionary bonus. In addition, we offer comprehensive and competitive benefits to Associates (and their families) such as medical, dental, vision, life insurance, short-term and long-term disability. Eligible Associates are able to enroll in our company's 401k plan. Associates will accrue paid time off up to 236 hours per year (inclusive of PTO, floating holidays, and paid holidays). Paid sick time up to 80 hours per year unless otherwise required by law.

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