Under such a strategy, it's crucial to make two correct project selections over 10 years. The actions involve:
- Buying one property and, after a year, evaluating it to gain extra funds from its increased value. You then purchase another property in the same market (or project) and continue this process for five years.
- In the fifth year, you sell everything and invest in market #2 (or project #2), and repeat the process for another five years.
Other strategies, such as renovation, construction, subdividing, or changing the property's use, often involve a significant amount of manual labor, paperwork, and additional complexities. This strategy focuses on the accessibility of all markets worldwide and the right choice, which doesn't require significant effort and isn't dependent on government authorities that can disrupt even the most promising investments.
Important Notes: In both the first and second strategies, it's crucial to:
- Make the right project selection.
- When buying during the construction phase, avoid defaults (choosing markets with escrow accounts is one option).
- Consider entrusting additional due diligence of the developer to AI, which can reduce these risks but not eliminate them.
- Refinance promptly and buy additional properties in the same project when necessary.
Conclusion
Can AI significantly increase the chances of successfully selecting the market and project? Absolutely. By analyzing all markets and projects, AI can compare prospects in different locations, which is not feasible for humans due to the vast amount of information. AI can also predict when the market's potential is diminishing and it's time to exit a project. This becomes noticeable in advance on the AI's dashboard and forecasts regarding market and project prospects.
In my estimation, by 2025, more than 50% of users will already be informed about AI in real estate. By 2030, not using AI in real estate decision-making will be as unusual as driving without a navigator is today. By that time, the quality of AI recommendations and insights will either be on par with the top 1% of experts or even surpass them.
Creating AI in real estate, as
Realiste did, is challenging due to the scarcity and complexity of data. The process requires overcoming obstacles related to data quality and abundance, making it a complex endeavor that not everyone has accomplished.
If you're interested in AI in real estate and want to learn more, feel free to reach out to our founding team.