Every developer understands the challenge of determining project viability before committing significant capital. After identifying a promising site and developing an initial concept, the next step typically involves engaging consultants to analyze market conditions, model absorption rates, and stress-test financial projections across multiple scenarios. The traditional feasibility study process takes up to six weeks and can cost tens of thousands of dollars. By the time the analysis is complete, market conditions may have shifted. Interest rates fluctuate, construction costs change, and competitive projects enter the pipeline. The expensive study that took weeks to produce can be outdated before ink dries on the binding. For large developers with dedicated in-house analytical teams, this represents a manageable friction point in the development process. For smaller firms, it can determine whether a project moves forward at all.
Commercial real estate developers have long relied on feasibility studies to determine whether a project makes financial sense, but the process has traditionally been slow, expensive, and quickly outdated. Now artificial intelligence is changing the economics of these critical analyses, potentially opening the door for smaller developers to tackle more complex projects.
There are two main types of feasibility studies in commercial development. The first covers architecture and engineering considerations, examining whether a project is physically possible to build. The second type focuses on market and economic feasibility, analyzing whether a project can be profitable given current market conditions, construction costs, and financing options. Economic feasibility studies typically occur either before a project starts or later in the process once the design is complete and cost estimates are finalized. Investors often require these studies, and some even demand third-party analysis before committing capital. The challenge is that these studies are expensive and can take up to six weeks to complete, during which time economic conditions can shift significantly.
AI can dramatically compress that timeline and reduce costs. “AI can do a verifiable analysis in days for only a fraction of the cost that can easily be updated as market conditions change,” said Brian Connolly, Founder and CEO of Feasibly. The technology excels at retrieving and analyzing large volumes of data, from local rent comparables and absorption rates to construction costs and zoning requirements. This capability is particularly valuable for small to midsized development teams. Unlike large ownership or development groups that maintain in-house teams for financial analysis, smaller companies typically lack dedicated staff for this specialized work. They have historically been forced to either pay for expensive third-party studies or proceed with limited analytical support.
The need for more sophisticated feasibility tools is growing as smaller companies pursue increasingly complex projects. Many are now developing mixed-use properties, which present significantly more analytical challenges because they involve multiple real estate asset classes operating within the same building. A single mixed-use development might include ground-floor retail, market-rate residential units, affordable housing receiving density bonuses from municipal authorities, and commercial office space on upper floors. Each component has distinct revenue characteristics, financing structures, and market dynamics that must be analyzed separately and then integrated into a comprehensive financial model. “Small to midsized teams are looking to do more complex projects that usually come with a public/private partnership component so they will need a lot of help understanding what exactly they can build profitably,” Connolly said.
Building AI tools for feasibility analysis requires more than simply applying general-purpose large language models to real estate data. “The misconception is that ChatGPT can do this without a lot of training. The problem is too much data, we needed to focus the decision making on the right part of the data,” Connolly explained. Effective AI feasibility platforms require training on datasets specifically relevant to real estate development, including local market conditions, construction cost databases, financing structures, and regulatory requirements. The technology must be calibrated to identify which data points are most material for different project types and how to appropriately weight various factors when market conditions contain uncertainty.
The implications extend beyond operational efficiency and cost savings. If AI can make feasibility studies faster, cheaper, and more accessible, it fundamentally alters the competitive landscape in real estate development and expands the universe of viable projects. Smaller development teams that previously could not justify the cost of comprehensive market analysis may pursue opportunities they would have otherwise abandoned. Developers can evaluate multiple scenarios for the same site to identify the optimal mix of uses rather than committing to a single program early in the predevelopment process. Most significantly, more firms may undertake mixed-use and affordable housing developments, which have historically been the province of larger institutional developers with sophisticated in-house analytical capabilities and the organizational resources to navigate complex public/private partnerships.
These changes could have meaningful implications for urban development patterns. Most major markets face acute housing shortages, and municipal governments are increasingly willing to provide density bonuses and other incentives for projects that include affordable units. However, many smaller developers with deep knowledge of local market dynamics have avoided these opportunities because the feasibility analysis required is prohibitively complex and expensive. If AI reduces those barriers to entry, we may see more locally-focused developers building mixed-income, mixed-use projects that create economically diverse neighborhoods rather than optimizing purely for financial returns. The technology will not resolve the housing affordability crisis independently, but it could enable a broader universe of developers to determine how to build profitably while also delivering the housing products cities need. In an industry where feasibility analysis has long served as a barrier to entry, particularly for smaller firms and complex project types, that represents a meaningful structural shift in how development capital gets allocated.
