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Real Estate Analytics

Full Guide

Since 1989 in data analytics, ScienceSoft builds custom real estate analytics systems that provide a 360-degree view of business performance and drive accurate insights for property evaluation and management, service personalization, informed investment decisions, market analysis, and more.

Real Estate Analytics - ScienceSoft
Real Estate Analytics - ScienceSoft

Real Estate Analytics: The Essence

Real estate analytics is needed to drive insights from property, customer, and market data and use these insights to make informed decisions on buying, selling, developing, and managing property. Custom real estate analytics solutions allow companies to provide their clients with highly relevant property options, identify profitable investment opportunities, oversee construction and property management, and stress-test business strategies using what-if models.

Who can benefit from real estate data analytics

Real estate brokerages, property investors and insurers, real estate appraisal firms, property management companies, real estate developers, urban planners and government agencies.

Essential integrations

Real estate database software, property research and internet listing services, CRM, accounting software, marketing management software, social media and customer review platforms.


$100,000–$600,000, depending on analytics complexity and data sources. Get a custom ballpark estimate for your case with our free calculator

Annual ROI

Up to 440%.

Key Features of Real Estate Analytics

Below, our experts list analytics features that our customers from different areas of real estate request. The actual capabilities will differ depending on your business specifics and analytics needs.

Reporting and visualization

  • Interactive dashboards with capabilities for slicing and dicing, drilling up and down.
  • Scheduled and ad hoc reports creation.
  • Visualizing location analytics insights on maps (e.g., amenities and crime rates, area-specific number of properties for sale or rent).
  • Automated submission of reports in regulatory-compliant formats (e.g., IRS Form 1065 and 1120-REIT for the US, HMRC Form SDLT for the UK) to regulatory authorities.
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Property valuation

  • Automated multidimensional property segmentation (e.g., by location, size, condition, available amenities).
  • Comparing property prices against user-defined factors (e.g., property attributes, similar-type property prices).
  • Automated pre-filling of real estate appraisal forms.
  • AI-powered assessment of property opportunity (e.g., likelihood of depreciation, value increase).
  • Monitoring market trends like supply and demand dynamics and mortgage interest rates.
  • Property value forecasts and what-if models.
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Property management

  • Occupancy analytics (e.g., occupancy rate per square foot, high-traffic zones, occupancy heat maps).
  • Lease management analytics, including lease expiration tracking per rentee, the influence of lease terms on lease renewal/termination.
  • Identifying bottlenecks in energy usage, water consumption, and waste generation across property portfolio and providing root-cause analysis.
  • Predictive property maintenance based on sensor data analytics.
  • AI-powered recommendations on optimal property management decisions (e.g., prompts on lease renewal opportunities, optimal space allocation).
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Real estate portfolio analytics

  • Tracking portfolio KPIs (e.g., cash flow, capitalization rates).
  • Multidimensional property segmentation (e.g., by property type, location, market segment).
  • Portfolio risk exposure analysis with risk sensitivity calculation and risk attribution analysis.
  • Stress testing what-if models of portfolio performance under various market conditions.
  • AI-powered recommendations on risk mitigation (e.g., optimal portfolio rebalancing based on concentration risks analysis).
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Pricing analytics

  • Analyzing custom pricing strategies using data on historical sales, current economic indicators, etc.
  • Assessing the performance of current pricing strategies.
  • Scenario planning and sensitivity analysis to assess the impact of various pricing strategies on business performance.
  • Dynamic pricing adjustment based on user-defined rules and real-time data inputs (e.g., changes in competitor prices).
  • Automated alerts on pricing-related events (e.g., deviations from user-defined pricing thresholds, related market changes).
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  • Customer segmentation (by demographics, family size for homebuyers, property type for sellers, and lease terms for tenants).
  • Identifying customer preferences (e.g., location and property types, rental rates).
  • Suggesting customer-specific property items.
  • Automated buyer-seller matching.
  • Mortgage pre-qualification.
  • Monitoring customer loyalty and satisfaction.
  • Identifying high-priority customers (e.g., high-value segments, late-to-pay tenants).
  • Creditworthiness analysis.
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Marketing analytics

  • Automated prospect segmentation (e.g., by demographics, property-related preferences).
  • Insights into prospects' online behavior (e.g., website searches, interaction with property listings).
  • Tracking KPIs across all marketing channels (e.g., online ads, email, social media).
  • AI-powered dynamic personalization of website content based on viewer-specific preferences and previous interactions.
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  • Monitoring key business metrics like sales volume, ROI, rental yields.
  • Analyzing customer service quality (e.g., issue resolution time, accuracy of property information provided).
  • Monitoring agents' performance (e.g., listing-to-sale ratio, average commission rate per agent).
  • Comparing agents' compensation and incentives against their performance.
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  • Tracking cash flow from operations, net operating income, and other financial metrics.
  • Monitoring portfolio KPIs (e.g., profitability per square foot, debt service coverage ratio (DSCR), cost performance index (CPI)).
  • Analyzing operating expenses like maintenance expense ratio and utility expense as a percentage of revenue.
  • Tax analytics (e.g., what-if models of tax liabilities, property tax comparison).
  • Forecasting financial outcomes through historical data analytics and what-if modeling.
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Compliance analytics

  • Monitoring the updates of regulations related to zoning and land use, environment, accessibility standards, and building codes.
  • Monitoring financial transactions, client information, advertising and marketing materials, etc. to ensure the adherence to the required regulations on anti-money laundering, fair housing, and data privacy.
  • Regulatory non-compliance alerts.
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Construction and development analytics

  • Analyzing potential construction sites using data on zoning regulations, available infrastructure, demographics, etc.
  • Construction cost calculation and segmentation (e.g., by labor, materials).
  • Tracking construction management metrics (e.g., total recordable incident rate (TRIR), time-to-completion, defects per unit, equipment utilization rate).
  • Identifying inefficiencies in construction management (e.g., budget overruns, quality control issues).
  • Building and stress-testing what-if scenarios to assess construction viability under different conditions (e.g., market events, construction delays).
  • Supply chain management analytics.
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  • Investment due diligence based on the analysis of lease agreements, property condition reports, etc.
  • Assessing the viability of a potential investment against an investor's risk tolerance and return objectives.
  • Providing alerts on potential risk-incurring aspects of an investment (e.g., discrepancies in lease agreements, unresolved maintenance issues).
  • Analyzing market data (e.g., area-specific number of property sales, auction rates and days on the market, vacancy rates).
  • Comparing objects of potential investment across user-defined factors.
  • Building investment-related forecasts (e.g., supply-demand fluctuations, property value, rental income).
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A Sample Real Estate Analytics Dashboard

See how business intelligence and analytics solutions can help navigate the variety of real estate data in a single interactive dashboard. Multiple filters make it easy to find property that corresponds to the required parameters, while clear graphs allow for insightful comparison of the chosen items. The sample also demonstrates the convenience of visualizing property objects on the map.

Essential Integrations for Real Estate Analytics

Integrations for real estate analytics largely depend on the business type. Below, we provide examples of both universal and business-specific systems that can be integrated with analytics software.

Integrations for Real Estate Analytics Software - ScienceSoft

Property research and internet listing services

E.g., Zillow, local MLS services.

  • To enable property value analysis.
  • To get insights into competitor activity and enable comparative market analysis.
  • To build and adjust pricing and investment strategies.

Geographic information systems (GIS) and public agencies

E.g., Google Maps, Esri, USGS, OpenStreetMap.

  • To get location-specific insights and enable their visualization on maps.
  • To support spatial analysis.
  • To enable customer segmentation and analyze customer preferences.
  • To get AI-powered recommendations on customer experience personalization.

Social media and customer review platforms

  • To analyze customer sentiment towards own and competitor services.
  • To get insights into revenue generation and cash flow management.
  • To enable accurate revenue and expense recording in the general ledger.

Property management system

  • To get insights into rental property performance.
  • To enable predictive property maintenance.

Regulatory agencies databases

  • To support compliance with zoning, land use, environment, and accessibility standards regulations (e.g., Clean Air Act, ADA for the US).

Construction management platform

  • To enable continuous project performance monitoring.
  • To track adherence to the defined budget and timeline.

Implement Real Estate Analytics with ScienceSoft

ScienceSoft's team of data analytics consultants, solution architects, software developers, and compliance experts can build a reliable real estate analytics solution tailored to the specifics of your business.

See What Insights an International Real Estate Company Got with Our Analytics Solution

Below, you can see dashboards that are part of an analytics solution developed by ScienceSoft. The dashboards are based on data from 40 sources and allow the company to get insights into its financial management processes, spot trends, and quickly react to changes.

Easy comparison of income and revenue across time periods

A clear picture of expense categories and shares

Easy-to-track profit dynamics

Comprehensive cash flow monitoring

A bird’s eye view of current assets and liabilities per account type

What Can Increase the Efficiency of Real Estate Analytics

Advanced customer-property matching

To offer customers highly relevant property options, an analytics solution should have matching mechanisms supported by data from multiple customer touchpoints. This data includes communication logs, website behavior and search history, historical matching results for same-type customer segments, and more.

Visualization of location-specific data

Since most real estate data is closely bound to a specific location, a good practice is to reflect location-related insights right on the maps. E.g., visualizing crime rates and natural hazard levels to quickly estimate risk factors or representing neighborhood demographics data to get a clear idea of the target audience in the area.

User-centric dashboards

An analytics solution should feature dashboards tailored to the unique responsibilities of different employees. For example, real estate agents are likely to appreciate capabilities for listing, sales, and lead tracking to facilitate pipeline management, while property managers may need lease expiration alerts and features for maintenance request monitoring.

Costs and ROI of Real Estate Analytics Implementation

The cost of real estate analytics implementation can vary from $100,000 to $600,000+ and depends on the complexity of analytics features, the quality and amount of data to be integrated, the need for big data and ML/AI techs, and other factors.

Data analytics in real estate can bring the annual ROI of up to 440%, with some of the drivers including capabilities for predictive property maintenance, market analysis, analytics of location-specific factors, and risk estimation.


A basic solution that enables:

  • KPI monitoring across 1–2 analytics areas, e.g., finance.
  • Integration with 1–2 key data sources, e.g., a real estate database.
  • Batch data processing (e.g., every 24 hours).
  • Scheduled and ad hoc reporting.


A solution of medium complexity that enables:

  • KPI monitoring across multiple business areas, e.g., customer management, construction management, etc.
  • Integration with 3–7 data sources.
  • Batch and real-time data processing.
  • Root-cause detection and forecasts based on non-neural-network ML models.
  • Automated reporting to regulators.


An advanced solution that enables:

  • KPI monitoring across all business areas.
  • Integration with multiple internal and external systems, including blockchain-based software.
  • Real-time big data analytics (e.g., for dynamic marketing content optimization).
  • Root-cause analysis and forecasting based on advanced ML models.
  • AI-powered personalization recommendations.
  • Custom reporting in regulatory-compliant formats.

Want to know a more precise figure?

Use our free online calculator to describe your case and get a custom quote.

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Benefits of Real Estate Analytics

Up to a 460% lead capture increase

due to customer segmentation and offer personalization.

Successful identification of up to 70% of customers

who are ready to buy property due to historical data analytics.

Up to a 90% reduction in building repair costs

due to preventive maintenance.

Up to 6% savings in construction costs

due to AI-powered financial modeling.

Up to 35% savings in cooling and heating costs

due to sensor data analytics.

Consider ScienceSoft for Real Estate Analytics Implementation

ScienceSoft's #1 priority is to drive projects to their goals. With an in-house PMO and established project management practices, we achieve project success regardless of time and budget constraints, as well as changing requirements.

Consulting on real estate analytics

We can estimate the cost and ROI of a data analytics solution, design business-specific features, and select relevant techs and tools.

We can also assist you in improving your existing system (e.g., increasing forecast accuracy, integrating new data sources).

Go for consulting

Implementation of real estate analytics

We can build a scalable and secure analytics system that smoothly integrates with multiple sources. The software will provide insights on property, customers, market, construction opportunities and progress, and other aspects, depending on your business type.

Our solutions enable self-service reporting tailored to your employees' roles.

Go for development

About ScienceSoft

ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. Since 1989, we help companies across 30+ domains build tailored analytics solutions to get reliable insights on customer acquisition and management, business growth opportunities, and risk mitigation. Being ISO 9001- and ISO 27001-certified, we can guarantee top software quality and complete security of our customers' data.