Real Estate | Lucid Sphere AI

Key Features of Real Estate Analytics

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.

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.

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).

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).

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).

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.

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.

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.

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.

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.

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.

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).