Property markets run on information asymmetries. The players with better data move capital faster and capture better returns. We've built AI systems that close those gaps—valuations that are 8% more accurate than traditional appraisals, demand forecasts that predict market shifts before they happen, portfolio algorithms that optimize for return and risk simultaneously. DoHuub is the foundation. We're expanding it.
We help real estate investors, operators, and developers make smarter capital decisions with ML-powered valuations, demand forecasting, portfolio optimization, and modern tenant experience platforms.
Traditional appraisals take 2-4 weeks and cost $500-2000 per property. The market moves faster. And appraisals are surprisingly subjective—two appraisers can value the same property differently by 5-15%. We've built ML models trained on thousands of sales, rentals, and market comps that deliver valuations in seconds with confidence intervals. More accurate. Faster. Cheaper. Lenders trust them because the logic is transparent.
Most portfolios are allocated based on historical performance or manager conviction. What if you could model expected returns, interest rate sensitivity, and tenant turnover risk across hundreds of properties and optimize allocation mathematically? We've built portfolio optimization systems that model property-level risks and suggest rebalancing opportunities. One REIT reallocated $400M in capital to higher-return assets based on our models without disrupting operations.
Tenant portals built in 2005 still run on 2005 technology. Maintenance requests take days to process. Rent payments require checks or clunky interfaces. Tenants leave, rent drops, turnover increases. We've built modern tenant experience platforms—mobile-first, instant request submission, online payment processing, transparent maintenance tracking. One commercial property owner saw tenant satisfaction improve from 6.2 to 8.1 on a 10-point scale. Renewal rates improved. Vacancy fell.
Predicting rent growth, occupancy rates, and absorption in new markets usually happens in Excel. Someone builds a model, leaves the company, and knowledge walks out the door. We've built reproducible, transparent forecasting systems that ingest market data (new construction, population trends, employment, economic indicators), learn from historical patterns, and predict 12-36 months out. Your team owns the model. It improves automatically.
A property tech platform needed to value hundreds of thousands of residential properties daily for lending and insurance. Traditional appraisals wouldn't scale. We built an ML model trained on MLS data, property records, and comparable sales that delivered valuations within 3-4% of actual sales prices in most markets. Time-to-valuation: 3 seconds. Accuracy: 96% within 5%. The company uses this model in its core lending product.
A commercial real estate firm managing 50 million square feet was surprised by major maintenance failures—HVAC failures in summer, roof leaks in winter. Each was expensive and disrupted tenants. We built a model using sensor data (temperatures, humidity, equipment age, usage patterns) to predict maintenance needs 30-90 days in advance. Maintenance became scheduled, not emergency. Capital budgeting became predictable. Tenant satisfaction improved.
A multifamily operator was using uniform pricing across all units in a property. Similar units rented at different prices because of listing timing. We built a system that analyzes unit-level features (floor, views, noise exposure, natural light), comparable rents in the submarket, and current demand to recommend unit-by-unit pricing. Revenue per available unit increased 7% without increasing vacancy.
A regional real estate developer wanted to expand into three new markets but didn't know which would perform best. We built demand models for each market using construction pipeline, employment trends, population growth, and macro indicators like interest rate forecasts. We predicted 5-year rent growth, occupancy rates, and cap rates. The models guided capital allocation. Two of three markets outperformed predictions. One underperformed but the model flagged it early, allowing the firm to pivot strategy.
Distribution platform with location optimization
Multi-location delivery platform with location analytics
Our models are typically within 3-5% of actual sales prices in established markets with good comp data. Markets with limited sales data or unique properties are less accurate. For appraisals in regulated contexts (lending), we use models as a screening tool, not a replacement for licensed appraisers, but they dramatically speed up the process.
We predict rent growth, occupancy, and cap rates based on leading indicators—employment, construction pipeline, population, interest rates. If macro conditions suggest a downturn, our models reflect that. But we can't predict black swan events. We do flag when assumptions are being violated, which gives you time to adjust strategy.
Valuation models: 6-8 weeks if you have clean property data. Demand forecasting: 10-12 weeks depending on market data availability. Tenant experience platform: 12-16 weeks including integrations with your property management system. We give you a detailed timeline after assessing your current infrastructure.
Niche properties require more training data and custom features. We've worked in rural markets, industrial, mixed-use—the models adapt. What matters is data quality and sufficient comparables. We assess feasibility during the discovery phase.
We'll review your portfolio, your data infrastructure, and your strategic priorities. Then we'll show you where AI creates measurable returns without disrupting operations.