Today’s Real Estate: An era where professional real estate know-how is made public for free. Can AI become a partner for home buying consultations?
When considering buying a home, the first step is usually to consult with a real estate company. That conventional wisdom is starting to change little by little.
Recently, more consumers have begun consulting generative AI about property searches, mortgages, and purchase budgets. However, the answers provided by AI are not always correct. Especially in fields like home buying, where individual life plans and financial situations have a significant impact, general answers are not always the optimal solution.
In this context, an interesting initiative has been announced: a real estate brokerage firm is making the professional know-how it has accumulated available for free for use with generative AI. In this issue, I would like to introduce this news, consider the potential of open-sourcing professional knowledge, and discuss how real estate sales representatives should engage with customers in the AI era.
Reference and introduction of the news
On October 27, 2026, TERASS, a company that operates a real estate brokerage service, announced that it has open-sourced a “Home Buying AI Framework” to support decision-making when purchasing a home and has released it for free on GitHub. The framework released this time was built based on the knowledge accumulated by the company’s approximately 850 real estate agents nationwide through more than 100,000 consultations per year. It is a system that allows people who wish to buy a home to proceed with consultations in the same way a professional real estate agent would by having them input specific information into generative AI such as ChatGPT or Gemini.
What is distinctive is that it does not start by asking for property prices or preferred areas, but rather begins with essential questions such as “Why do you want to buy a home?” and “What kind of lifestyle do you want to achieve?” From there, it organizes information step-by-step regarding family structure, future life planning, financial status, and mortgages. In the company’s verification, the percentage of cases where generative AI was able to provide answers without misleading the consultant based on their situation improved from 24% before the introduction of the framework to 90% after. However, this is a verification result using 11 questions set by the company, and it does not guarantee the same level of accuracy for all home buying consultations. What is noteworthy is that they did not monopolize this know-how within their own company but made it widely available to the public.
The compatibility of professional know-how and open source
What is interesting about this initiative is that the experience and knowledge held by real estate professionals have been released in the form of open source. Open source generally refers to a system where the contents of programs and the like are made public, and anyone can participate in their use and improvement under certain rules. In this case, the process of conducting home buying consultations and the decision-making process have been made public, and it is designed to accept improvement proposals from outside.
In the first place, the real estate industry has long had a great deal of know-how that has relied on individual experience and intuition. Excellent sales representatives draw out potential needs while listening to customers and provide appropriate advice on the timing of purchases and financial planning. However, that skill has not necessarily been systematized, and there have been large differences in service quality depending on the company and the representative. Generative AI is good at verbalizing such personal know-how and turning it into a form that anyone can use. For example, if you organize the order in which experienced sales representatives ask questions to customers and what criteria they use to make judgments, even inexperienced representatives will be able to handle consultations at a certain level.
Furthermore, by open-sourcing it, it becomes possible to incorporate new knowledge from outside experts and users. Real estate market conditions, mortgage systems, and tax systems are constantly changing, and there is a limit to how much one company can keep information updated on its own. Being able to make improvements while incorporating external knowledge is a major advantage. On the other hand, does making know-how public not lead to a loss of competitiveness? Personally, I think there is a possibility of the opposite. The more basic knowledge and consultation procedures are widely shared, the more important the execution ability and proposal ability—how to utilize them—ultimately become. From an era where having knowledge itself was a competitive advantage, to an era where utilizing knowledge to provide value to customers is a competitive advantage. I feel that this initiative symbolizes such a change.
How should actual representatives engage with customers?
So, when generative AI becomes able to utilize professional know-how, what role will be required of actual real estate sales representatives? The first thing that will change is the point of contact with customers. Until now, there have been many cases where customers themselves had not sufficiently organized their desired conditions or purchase budgets at the stage of inquiring with a real estate company. However, in the future, there may be an increase in customers who come for consultations after having organized their wishes and financial plans to some extent through dialogue with generative AI. In that case, if sales representatives only explain the general flow of home buying or the mechanism of mortgages from the beginning, they may not be able to provide sufficient value to the customer. It will be necessary to make proposals that go one step further based on the information presented by the AI.
For example, even if a property is judged to be purchasable based on a financial plan by AI, there are cases where it is better to forgo the purchase when considering future education expenses, the possibility of changing jobs, or the need to care for parents. Also, even for properties that meet the desired conditions, there are many factors that can only be judged by checking on-site, such as the surrounding environment, management status, and the condition of the building. Carefully confirming these individual circumstances and sometimes making proposals that differ from the customer’s wishes is also an important role for a professional.
And another important thing is the relationship of trust with the customer. For many people, buying a home is one of the biggest purchases of their lives. No matter how many rational decision-making materials are available, anxiety and hesitation will arise in the end. What is needed at that time is not just conveying correct information. It is an attitude of understanding the customer’s anxiety and thinking together until they are satisfied. In the future, real estate sales representatives will be required to have an attitude of utilizing AI together with customers, rather than viewing it as a competitor. If the burden of information gathering and condition organization is reduced by AI, they will be able to spend that time on dialogue with customers, on-site surveys, and concrete proposals.
TERASS’s initiative this time can be said to be not just the release of an AI utilization service, but a questioning of the nature of professional knowledge in the real estate industry. When professional knowledge becomes available to anyone, where does the value of a sales representative remain? The answer may lie not in the amount of knowledge, but in whether they can understand the circumstances of each individual customer and support optimal decision-making.
Thank you for reading to the end!!
In this note, I organize real estate industry news, system revisions, and changes in market conditions every day from the perspective of management and the field. If you follow me, you can check sequels and related issues on the same theme all at once!
I also regularly upload interview articles and coverage articles that are useful for management decisions. Please also register for the paid magazine where you can read all past articles!
