While artificial intelligence is being piloted to analyze environmental and property data during due diligence, AI is also influencing broader real estate development considerations. Emerging applications are reshaping how development teams evaluate potential sites, analyze zoning constraints, and assess redevelopment feasibility earlier in the project lifecycle. Increasingly, developers, investors, and real estate technology platforms are using AI to synthesize zoning, geospatial, infrastructure, and market data to support site selection. By enabling faster screening of potential redevelopment sites and identifying regulatory or physical constraints that may affect feasibility, risk, or transaction outcomes, these tools are influencing development decisions upstream of the formal due diligence process.
Site Selection and Feasibility
As real estate becomes more data-driven, artificial intelligence is playing a greater role in generating more comprehensive risk profiles for individual properties. AI-enabled geospatial tools like ESRI ArcGIS and Google Earth Engine can integrate and analyze maps, satellite imagery, zoning data, environmental variables, infrastructure, and demographic trends, drawing on public and proprietary datasets to identify optimal development sites and evaluate long-term viability. By rapidly synthesizing large volumes of geospatial, regulatory, and market data, these tools enable developers to make earlier, more informed site-selection decisions and uncover underutilized or redevelopment-ready parcels.
These capabilities are often integrated into PropTech (property technology), a growing market sector comprising feasibility platforms that allow investors and developers to evaluate potential projects more efficiently. By integrating market data, zoning and geospatial inputs, and infrastructure and environmental considerations, these platforms can model development scenarios, test assumptions, and highlight location-specific risks. Many also include collaborative features that help project teams share insights and refine analyses in real time. For example, Deepblocks is an AI-powered platform that analyzes financial and market data along with local zoning regulations to provide real-time site optimization and feasibility assessments.
Other platforms include Archistar Property’s Generative Design Engine, which uses up-to-date aerial imagery and data to help developers find suitable sites while identifying relevant regulatory requirements or development agreements already in place. CityBldr helps users find underutilized land and determine its best use. The nonprofit National Zoning Atlas centralizes zoning information for more than 33,000 U.S. jurisdictions and translates it for the public.
Market Analysis and Redevelopment Strategy
Artificial intelligence is reshaping how developers and investors analyze market conditions and evaluate redevelopment opportunities. By processing large volumes of historical transaction data, demographic information, and current market indicators, AI systems can detect patterns that may signal future shifts in pricing, demand, or vacancy rates. These analytical capabilities allow real estate professionals to assess project feasibility, estimate development costs, and model potential returns earlier in the decision-making process. AI tools can also highlight cyclical or seasonal market dynamics, helping investors better time acquisitions, dispositions, or redevelopment efforts.
AI tools can also highlight cyclical or seasonal market dynamics, helping investors better time acquisitions, dispositions, or redevelopment efforts.
AI can support regulatory and financial analysis by modeling project economics, forecasting demand, and identifying potential issues in title or transaction records, providing development teams with deeper insight as projects move toward execution. StateBook, another AI tool, provides comprehensive economic data, including federal, state, and local tax incentives, allowing users to compare locations and identify the most strategic investment opportunities.
Several prominent real estate and financial institutions have begun incorporating AI-driven analytics into their operations. For example, Skyline AI and Zillow employ machine-learning models to analyze large real estate datasets and generate market insights. Major industry participants are also adopting AI to support internal decision-making and operational efficiency. CBRE uses AI-based analytics for pricing forecasts, predictive modeling, and portfolio management, while Bank of America has implemented AI technologies to automate portions of the loan application process.
Public-Sector Applications
In addition to private-sector adoption, local governments are beginning to explore how artificial intelligence could support municipal planning and regulatory functions that affect real estate development. Several U.S. municipalities are testing AI tools to help streamline tasks such as construction permit reviews, development application processing, and aspects of code enforcement. Proponents suggest that these systems could improve efficiency and consistency in planning departments by assisting with routine administrative work, analyzing land-use data, and potentially drafting zoning guidance or other planning resources. While such tools could ultimately help accelerate the development approval process, most governments are still evaluating the technology through pilot programs as they assess its reliability and limitations.
AI is well-suited for real estate, where the industry relies on large datasets, repeatable processes, and extensive documentation, all of which can be optimized through predictive analytics and generative tools.
AI may also assist regulators in prioritizing environmental compliance oversight. With hundreds of thousands of permitted facilities subject to federal environmental regulations, including industrial operations and fossil fuel power plants, government agencies face practical limits on inspection capacity. Some researchers have suggested that AI models capable of analyzing regulatory data, operational records, and other indicators could help identify facilities that are more likely to be out of compliance, allowing regulators to focus inspections where potential risks are greatest.
Balancing Technology and Expertise
Artificial intelligence is well-suited for real estate, where the industry relies on large datasets, repeatable processes, and extensive documentation, all of which can be optimized through predictive analytics and generative tools. As a result, AI is reshaping how redevelopment opportunities are identified, enabling developers and investors to assess site suitability more efficiently. However, AI tools rely on datasets that can be incomplete, outdated, or unable to fully capture site-specific environmental conditions. These tools also cannot interpret context beyond the data, which is why expert analysis is essential to ensure that information is reliable, understood in a regulatory context, and that initial assumptions hold through transaction, permitting, and construction stages.
As AI becomes more integrated into real estate decision-making, it raises governance considerations around data privacy, security, and algorithmic bias, making client consent and ongoing auditing essential to support fair and reliable outcomes. While AI may accelerate how opportunities are identified and evaluated, experienced environmental professionals continue to play a critical role in confirming risks, interpreting requirements, and translating technical findings into practical guidance for development teams.
…experienced environmental professionals continue to play a critical role in confirming risks, interpreting requirements, and translating technical findings into practical guidance for development teams.
Looking ahead, continued advances in machine learning and data analytics are expected to expand AI’s role in commercial real estate. Broader adoption will depend on stronger data governance practices, clearer ethical frameworks for AI deployment, and robust cybersecurity safeguards to support responsible and reliable use across the industry.
Editor’s Note: ERIS continues to explore how AI can improve data quality and increase efficiencies to support environmental professionals and their environmental due diligence decision-making. If you’re interested in how ERIS is evaluating and integrating these tools in practice, please contact Diana Saccone.
Mary Ann Grena Manley
Founder and President of 15E Communications LLC, Washington, DC
Mary Ann is the Founder and President of 15E Communications LLC, a Washington, DC-based consulting firm that assists clients with communications strategy, content, business development, and public relations. Prior to founding 15E in 2020, Mary Ann managed Bloomberg Industry Group’s coverage and analysis of global environment, health, safety, and sustainability issues for more than 20 years. With experiences cutting across environmental law, policy analysis, journalism, and marketing, she most recently served as Deputy Editorial Director for Bloomberg Environment. Her areas of policy expertise include environmental compliance, environmental due diligence, risk management, brownfields redevelopment, and sustainability. Connect with Mary Ann via email, or through LinkedIn.
