AI in Real Estate: Transforming Property Investment, Valuation, and Customer Experience

Applications of AI in Real Estate

AI offers enormous opportunities to revolutionize the real estate sector. In this section, we analyze some of the most compelling use cases of AI in real estate.

Smart building and design

AI is rapidly democratizing architecture, making the process of design easier and more accessible. Generative design is a booming field that encompasses using AI algorithms to generate and evaluate multiple design alternatives based on input from the user. 

Specialized tools like Maket allows anyone to design and plan their new build or renovation project in a few simple steps. With the help of generative AI, you can create floor plans, architectural renderings and interior design, based on plain natural language prompts. 

Equally, fine-tuned generative AI models can help you navigate building regulations and make informed decisions in aspects such as performance requirements, manufacturing processes, and materials.

Listing descriptions

Good descriptions are crucial when it comes to selling or renting a property. However, writing listing descriptions is normally a manual, time-consuming process. Fortunately, generative AI is making listing descriptions faster and more accurate. 

Tools like ListingAI use generative AI to create unique and compelling descriptions of real estate properties in a matter of seconds, saving time and money for real estate agents and boosting SEO positioning.

Property evaluation

What is the real value of a property? This is an extremely complex question. Many variables could be considered to estimate the exact value of a property, including location, features of the asset, demographic shifts, and environmental risks. 

Fortunately, AI is changing the way owners and investors value property. Powerful machine learning models can now analyze large amounts of data to arrive at property valuations that are not just estimates but reflections of true, up-to-date market value.

A great example of how AI can help real estate professionals during asset evaluation is StadimData, a company that has created a platform to easily collect real estate data and automatically predict property value. 

Real estate Investment 

AI and predictive analytics are rapidly changing decision-making in real estate investment. Thanks to powerful machine learning models, investors can now feed their models with highly granular data, such as location, condition, market demand,  real-time market trends, economic indicators, and even social media sentiment. 

Big players in the real estate sector, like BlackRock, leverage these models to have a more nuanced view of property value, as well as spot investment opportunities at a global scale, for instance, in new housing construction, residential real estate, and mortgage funding. 

Customer service

AI provides an effective and efficient way for real estate companies to expand their capacity to serve more customers at a significantly reduced cost. By replicating and augmenting human capabilities, AI-powered chatbots can collect and cross-reference information to develop responses and solutions customers may get during the buying or renting journey. 

AI-powered customer service tools, like Phonely, reduce the workload for realtors while ensuring that customers get the support they need when they need it, by handling inquiries, scheduling property viewings, and offering personalized recommendations. 

Check out our separate article to discover everything about AI in customer service.

Strategic Implementation of AI in Real Estate

Despite everyone in the real estate room recognizing the opportunities of AI, realizing the full potential of these technologies remains the biggest challenge for the years ahead. 

According to JLL's 2023 Global Real Estate Technology Survey, only 13% of respondents consider themselves early tech adopters, actively piloting or deploying new technologies, while 32% would like to do so shortly but lack a clear strategy, skills or budget for effective deployment. 

While the historic reluctance of real estate companies to embrace innovative technology may be an important variable in understanding the difficulties of the sector, it’s also fair to say that they are not the only ones struggling to realize the value and competitive advantage of AI. Implementing AI in your business operations is often a long journey full of obstacles. Many things have to go right in an organization to make the most of the AI opportunity.

In this section, we cover the main strategic actions real-state players can take to ensure a successful implementation of AI

Integrating AI tools and platforms

Integrating AI tools and platforms is a great challenge for companies. Many questions may arise during the process. When and where to use AI? What tools to use? Is it worth the cost?

Raj Singh, Partner at JLL Spark, points out the main elements that can make a difference when it comes to AI integration:

Here is a list of the steps for integrating AI into your business operations:

Identify business problems

Yet cutting-edge tools like ChatGPT can potentially serve a wide range of applications, the truth is that most AI applications work best for specific use cases. In this vein, a great starting point is to identify particular inefficiencies in your workflows where the implementation of AI systems could make a difference.  

Try out AI tools

Once you have spotted areas of improvement, it’s time to invest time and money to try different AI tools. Ideally, you implement an AI stack that covers the whole data analysis process, from data collection (i.e. extracting data from various sources and formats) and storage to data interpretation and visualization. The goal of this experimentation phase is to familiarize your teams with different AI tools, noting their features, strengths, and weaknesses. The AI landscape for real estate is rapidly evolving and it’s important to find the right fit for your company. 

Align AI tools with priorities and resources

AI offers many opportunities to improve your business, but you need to be strategic. Implementing these tools will take resources and time. At this stage, you need to strike a balance between your priorities, money, and human resources. That analysis is crucial to deciding which AI tools you want to implement. 

Furthermore, as Philipp Herzig, Chief AI Officer (CAIO) at SAP, pointed out in our article on Chief AI Officers, 

Evaluate risks and compliance

The sooner you think about the potential risks of AI tools, the better, but you must conduct this analysis before implementing any of these tools. AI comes with enormous potential benefits but also with some risk. 

Ensuring the ethical and responsible use of AI is not only a smart decision but also a rapidly becoming legal obligation. Countries worldwide are advancing data protection and AI regulations (for example, the EU recently approved the EU AI Act), which require companies to implement comprehensive measures to ensure compliance. 

Integrate AI and measure success

Now, you’re ready to integrate AI tools into your business. In this final stage, it’s important that you have developed a strategy to measure success. In other words, you need to establish some sort of metrics or KPIs to measure your return on investment. You can read more about the ROI of AI in our separate guide. 

Change management and training

Whatever the AI solution you want to implement in your real estate company, you will need your employees to make that happen. That’s why, before starting the process, it’s essential to conduct an honest evaluation of your team's capabilities, needs, and existing processes. 

Below, you can find a guide on how to equip your teams with essential AI skills.

Assess current skills to identify gaps

The first step in launching a successful data analytics training initiative is assessing your team's current skill levels to identify gaps and determine specific training needs. Conducting a comprehensive skills assessment allows organizations to understand the proficiency levels of their employees in data analytics and related technologies. 

This evaluation can be achieved through surveys, interviews, and practical assessments to gauge knowledge, expertise, and familiarity with various data analytics tools and techniques. You can also use DataCamp’s data maturity assessment to understand where your team, department, and organization are located on the data maturity spectrum.

Tailor training to address specific needs

Understanding your team’s current skill set allows you to customize training programs effectively. By focusing on areas that require improvement, you ensure that your training is relevant, targeted, and impactful. Collaboration with your data analytics academy partner, such as DataCamp For Business, can facilitate this process. 

Our expert teams can help you tailor existing learning tracks or create new ones to meet your organization's specific needs and ensure that all employees stay on track with their learning goals. 

Define metrics for success

Establishing clear metrics and methods to measure the success of your training program is crucial. Define key performance indicators (KPIs) and objectives that align with your business goals, such as enhanced analytical skills, increased productivity, or improved decision-making capabilities. These metrics will help you track progress and demonstrate the impact of your training efforts.

Be proactive about change management and positive reinforcement

Introducing AI and data technologies into the workplace can cause anxiety and resistance among employees. Effective change management strategies are essential to address these concerns. Leaders should clarify the benefits of AI and data upskilling, proactively address fears related to automation, and celebrate early adopters who champion these initiatives.

Andrew McAfee from the MIT Sloan School of Management emphasizes the importance of changing toward a data culture, stating:

Creating a supportive environment that celebrates learning and adaptation helps mitigate resistance and fosters a culture of continuous improvement.

Utilize comprehensive resources

At DataCamp, we understand the complexities of building a data analytics academy and are here to support you every step of the way. We offer a wealth of data literacy resources, including blogs, webinars, and use cases specifically designed for business leaders.

Benefits of AI in Real Estate

Let’s analyze the main benefits of the adoption of AI in real estate:

Enhanced decision-making

AI tools can process information much faster and more accurately than humans. These technologies enable predictive analytics, pattern recognition in vast and diverse datasets, and automated decision-making. This enables faster responses to market changes, allowing businesses to act quickly on new real estate opportunities or mitigate risks.

Curious about how AI can support business decision-making? Check out our Data-Driven Decision Making for Business Course to learn all about it.

Operational efficiency

AI can be used internally to optimize the day-to-day operations of your real estate company. By detecting bottlenecks and inefficient processes, and identifying ways to streamline operations, AI can help your company boost its performance and reduce costs. 

At the same time, the automation of repetitive, time-consuming tasks will free up your real estate agents, who will have more time to focus on higher-value work. Also, if well implemented, AI could be the cornerstone of efforts to reduce working hours and increase your employees' satisfaction. 

Improved client experiences

Personalization driven by AI also plays a critical role in customer satisfaction. By tailoring experiences based on customer preferences and behaviors (for example, by creating floor plans and interior designs based on their preferences), real estate firms can provide better services, leading to stronger client relationships and increased satisfaction. The use of AI tools for customer engagement ensures faster responses, accurate property recommendations, and a more seamless client experience, ultimately driving loyalty and retention. 

Challenges and Considerations

While AI holds great promise, it also presents several challenges. Let’s analyze the main ones:

Ethical and privacy concerns

When it comes to AI use in real estate, ethical concerns are paramount! Practicing ethical AI is important for maintaining trust and fairness in real estate applications. Housing is the basis of stability and security for an individual or family. While AI can boost the numbers of the sectors, the revolution the sector has already embarked on cannot result in a limitation of the right to adequate and affordable housing. And this is a problem that we are already witnessing in many places around the world.

Issues such as AI bias, accountability, and the potential for misuse raise important ethical questions that need to be faced seriously to ensure that everyone, no matter their origin or economic background can freely buy, rent or sell a property.

On the other hand, data privacy remains a significant challenge. The use of AI requires access to large amounts of real estate data and personal data, which can raise concerns about data security and privacy. Robust data protection measures are essential to safeguard sensitive information.

In addition, compliance with the rapidly evolving legal landscape is mandatory for real estate companies to ensure data protection and fair and safe use of AI. A great example of how the future of compliance will look is the recently approved EU AI Act, a comprehensive regulation that requires companies, including insurers, to advance strict regulatory measures. 

Check out our EU AI Act Fundamentals Skill Track to learn about this innovative and ambitious rule and how to stay compliant.

Integration with existing systems

As we already mentioned, how integrating AI solutions with existing systems and making them scalable can be complex and requires modern infrastructure, which many real estate companies lack. 

The real estate sector has been historically a laggard in technology adoption. These companies usually have legacy systems that may not easily interface with cutting-edge AI tools. Combining machine learning and AI technologies into these established processes requires careful planning and customization to avoid disruptions. Insurance companies need to assess their current infrastructure, identify potential integration challenges, and invest in necessary upgrades to ensure that both systems work harmoniously with existing technologies.

Cost and ROI analysis

Evaluating the cost and return on investment (ROI) of implementing AI solutions is another factor. Developing and deploying AI solutions can be expensive, requiring significant investment in technology, infrastructure, and training. While AI can lead to cost savings and efficiency gains, the initial investment and ongoing maintenance costs must be carefully considered.

Future Trends in AI and Real Estate

Looking at the future of real estate reveals thrilling prospects for AI. According to the JLL's 2023 Global Real Estate Technology Survey, real estate companies believe artificial intelligence (AI) and generative AI as the technologies expected to have the greatest impact on real estate over the next three years. From an economic perspective, the numbers are impressive. The latest analysis from the McKinsey Global Institute predicts that gen AI could generate $110 billion to $180 billion or more in value for the real estate industry. 

The whole business has already embarked on a journey of transformation, with AI and other strategic technologies, such as the Internet of Things, 3D printing, and robotics, being the key drivers of change. However, to ensure a rapid and smooth transition, real estate companies and agents need to know how to use these powerful tools.  

As already mentioned, building a successful AI strategy requires considerable effort and resources. But even companies with big pockets fail to implement AI solutions. Why? Because they lack enough skilled employees with AI literacy. 

Successful implementation and management of AI systems require a skilled workforce. Data scientists, AI specialists, and other professionals with AI and machine learning expertise are essential for developing, interpreting, and managing these technologies. 

Fortunately, DataCamp is here to help insurers. With our DataCamp for Business solution, we can help your company become data and AI literate. With a scalable solution that can work for teams of any size, along with customizable learning paths and detailed reporting, DataCamp for Business can help you transform your business and become AI-ready. 

Conclusion

We hope you enjoyed this article. AI is one of the key drivers of change in the real estate sector. The application of AI and machine learning in real estate is already revolutionizing the sector, and more is about to come.

As these systems become smarter, real estate companies with a solid grasp of AI fundamentals are likely to hold a significant edge over their competitors. 

Request a demo to learn how DataCamp can guide you through the process of upskilling your entire team and building a data-positive culture. In the meantime, check our dedicated materials on AI:

• AI Business Fundamentals skill track 
• How to Learn AI From Scratch in 2025: A Complete Expert Guide
• AI Fundamentals Certification
• Hands-on data and AI training for financial services
• AI in Banking: How AI is Transforming the Banking Industry
• 10 Ways to Use ChatGPT for Finance
• What is the EU AI Act? A Summary Guide for Leaders
• EU AI Act Fundamentals
• DataCamp for Business Demo
• AI Integration: Top Tips For Integrating AI into Your Business
• What is AI Literacy? A Comprehensive Guide for Beginners
• Introducing The State of Data & AI Literacy Report 2024
• The Learning Leader's Guide to AI Literacy
• Leading platform for AI literacy across your entire business