Real estate development has long been seen as an industry that relies on experience and intuition, the so-called "gut feeling." This stems from its non-standard nature: the entire process from land acquisition to permits, financing, construction, and sales unfolds over a long period, and outcomes and profitability vary significantly depending on asset type and market conditions. But the spread of artificial intelligence (AI) is changing this premise. Real estate development is shifting from an experience-driven industry to one that verifies diverse possibilities based on data.
This change appeared first overseas. Opendoor, a real estate platform company, uses an AI pricing engine trained on millions of transaction records and field survey data to calculate home values in real time and automate purchase and sale decisions. As an area once dependent on expert experience shifts to a data-based system, transaction speed, accuracy, and profitability are improving simultaneously.
Zillow, another real estate platform, automatically calculates home values using a model that combines location, physical characteristics, transaction history, and image data, lowering its error rate to around 2 percent. The market, traditionally dependent on the experience of appraisers and brokers, is being reorganized around data.
Changes are also clear at the development and design stages. French construction firm Bouygues used generative AI design technology to simulate thousands of design proposals simultaneously and derive optimal structures. This reduced material usage and cut design periods and construction costs. At the operational stage, as "smart buildings" become commercialized, Johnson Controls has used an AI-based building platform to analyze building operation data in real time and apply automatic controls, cutting energy use by up to 30 percent and maintenance costs by about 20 percent.
In this way, AI is going beyond simple automation to change the decision-making structure of real estate development as a whole. In the past, key variables such as interest rates, construction costs, and sales rates were reviewed individually. Now, scenario analysis reflecting the interactions among various variables has become possible. It has become possible to predict and manage not only business viability but also risks such as construction delays and sales failures at the same time. Project financing (PF) has also become capable of designing a more stable funding structure by analyzing cash flows that reflect various market variables such as interest rate fluctuations and sales delays across multiple scenarios.
Ultimately, real estate development no longer stops at construction and sales. As it shifts from an "industry of experience" to a "calculable industry," it is expanding into a process of designing and maintaining stable structures amid diverse variables. From an investment perspective, it is necessary to focus on the capability to systematize decision-making based on data and control risk, rather than evaluating only the success record of individual development projects. Going forward, the level of AI utilization is likely to become one of the key indicators for gauging the competitiveness and profitability of real estate development companies.
