Commercial Real Estate Turns to AI to Automate the Back Office

Artificial intelligence (AI) is beginning to reshape commercial real estate—not through futuristic smart buildings, but by automating the manual back-office work that underpins valuations, underwriting, leasing and property operations.

Morgan Stanley estimates that AI could automate about 37% of tasks across the sector, unlocking as much as $34 billion in efficiency gains by 2030. As rising financing costs, tighter margins and slower deal activity push property owners, lenders and operators to find structural cost savings, the technology is increasingly being deployed to compress timelines, reduce human error and standardize decision-making across the real estate life cycle.

Valuation, Underwriting and Due Diligence Accelerate

Under pressure to move faster and operate with leaner teams, real estate firms are turning to AI in valuation, underwriting and due diligence, areas long dominated by manual analysis and spreadsheet-based models. AI models now ingest transaction data, market comparables, zoning rules, macroeconomic indicators and alternative data sources to produce dynamic valuations that update as conditions change.

JLL notes that AI-driven valuation models can incorporate real-time signals such as local economic activity, mobility patterns and supply constraints, allowing investors and lenders to respond faster to pricing shifts and risk exposure. PwC and the Urban Land Institute describe a similar trend in underwriting, where machine-learning systems automate document ingestion, risk scoring and scenario modeling, reducing friction in deal execution and shortening transaction cycles.

This automation is extending into private credit and nonbank lending. HomeSageAI recently launched an AI-powered property analytics platform aimed at hard-money lenders, using machine learning to assess collateral quality, borrower risk and neighborhood trends more quickly than traditional underwriting methods.

Leasing, Marketing and Ownership Models Evolve

AI is also reshaping how properties are marketed, discovered and leased. Instead of static listings and standardized tours, AI systems personalize property discovery by adapting recommendations, pricing and presentation to user preferences, budgets and behavioral data.

AI-generated listings can automatically tailor descriptions, imagery and pricing guidance for different tenant segments, reducing manual work for brokers while improving conversion rates. Virtual tours powered by computer vision and generative AI allow prospective tenants and buyers to explore properties remotely, expanding reach and reducing time on market, particularly for commercial and multifamily assets.

Beyond leasing, AI is beginning to influence ownership structures through tokenization and fractional ownership. AI is now being combined with blockchain to support continuous valuation, compliance monitoring and liquidity management for tokenized real estate and infrastructure assets. These models depend on AI to manage pricing, governance and risk at scale, functions that would be difficult to sustain with manual oversight.

As AI becomes embedded in core workflows, risk management has moved into focus. JLL cautions that real estate firms must address data quality, model transparency and cybersecurity as AI systems begin to influence pricing decisions, leasing strategies and capital allocation.

Looking ahead, AI’s role is expanding from analysis to execution. As PYMNTS recently reported, Aldar, one of the Middle East’s largest property developers, partnered with Visa to launch voice-enabled agentic payments, illustrating how AI agents could initiate and complete transactions through natural language commands. The pilot shows how agentic systems can move beyond recommendations into execution, managing rent collection, vendor payments and operational approvals without human intervention.

For a real estate operator like Aldar, which oversees large residential, commercial and mixed-use portfolios, the model signals a shift toward more autonomous property operations. Over time, AI agents could coordinate financial and operational workflows across property management, payments and accounting systems, reducing manual effort while accelerating decision-making and execution at scale.