Use cases of AI in commercial real estate
As AI technology rapidly advances, its applications in real estate and construction are becoming increasingly vital. Recognizing the potential at this stage, we are focusing on custom GPTs to facilitate everyday tasks in Commercial Real Estate (CRE) without coding. Although the most effective approach, we believe, is to develop bespoke GPTs tailored to specific business processes, these series of articles will delve into practical applications of publicly available GPTs, illustrating the concept of their use. Disclaimer: The GPTs mentioned are sourced from the GPT Store, and their outputs have not been independently verified by the author, as accuracy depends on their training.
Affordable housing has become a buzzword in many countries, increasingly recognized as a global issue. As AI continues to penetrate every conceivable process, it's inevitable that humanity will employ the most advanced tools to tackle this growing crisis. In this article, we will explore two of the numerous ways that GPTs can support the development and management of affordable housing solutions.
Navigating the complex regulations and eligibility requirements for funding in housing development involves substantial manual effort. By integrating these processes into generative AI, we can minimize the manual workload involved in determining eligibility and available programs. Localized tools are particularly effective. For example, we can create bots that encapsulate comprehensive knowledge of specific programs, such as LIHTC, a primary source of funding for affordable rental homes in the US.
Another approach is to develop a knowledge library of available programs, enhancing accessibility and understanding at the federal, provincial or local level.
In one instance, a bot provided specialized counseling services related to affordable housing within a local community.
The task of locating homes is well-suited for AI, especially given the complexities involved in finding affordable real estate. This process is particularly effective when highly localized, ensuring the relevance of search results. Below, we present examples of how AI can streamline the search for affordable rentals. The first example includes a prompt for units priced below $1,500 in Brooklyn, NYC. While we cannot guarantee the accuracy of these listings, as AI technologies improve, such tools could drastically reduce the need for manual searches.
A similar tool offers localized searches in Toronto, demonstrating how AI can potentially transform the search process for affordable houses.
Custom GPTs hold significant potential in streamlining the regulatory processes and enhancing decision-making within the affordable housing sector. They can identify optimal development sites, predict housing demand, and simulate the impact of housing policies. However, their effectiveness depends heavily on the quality and comprehensiveness of the data they are trained on. While GPTs provide valuable insights, final decision-making should integrate human oversight to consider broader social, economic, and political factors.
In conclusion, while AI and custom GPTs present innovative solutions to the challenges of affordable housing, they are not panaceas. Their successful implementation requires a balanced approach that combines AI's analytical capabilities with human expertise and oversight. This ensures that the solutions developed are not only efficient but also equitable and sensitive to the diverse needs of the community. As we continue to explore and expand the capabilities of AI in real estate, it remains crucial to approach these technologies with a blend of optimism and careful consideration.
