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AI in Real Estate and PropTech: Key Use Cases AI in Real Estate and PropTech: Key Use Cases

AI in Real Estate and PropTech: Key Use Cases

Aug 08, 2024

16 mins read

The real estate industry saw a rapid rise in investment at the end of 2023, driven mainly by a maturing proptech ecosystem and technological advancements. This resulted in over 80% of industry professionals planning to increase their real estate technology budgets within the next three years. AI and generative AI stand out among the technologies promising to make the most significant impact, outpacing others in hard dollar commitments. In the commercial real estate sector alone, 72% of organizations are currently either piloting, in early-stage implementation or already using AI-powered solutions.

These statistics prove that artificial intelligence can significantly influence the real estate sector in the coming years. Its ability to boost productivity and optimize budgets is capturing considerable interest in the market. The numbers speak for themselves: AI in real estate is projected to grow at an annual rate of 11.52%, potentially reaching approximately $1,047 million by 2032. McKinsey states that gen AI alone could contribute up to $180 billion to this growth.

Recent statistics on AI usage in real estate
Recent statistics on AI usage in real estate

Despite the recognized benefits of AI applications, the same McKinsey report states that many organizations in the real estate sector are struggling to effectively implement and scale AI use cases, thus delaying the realization of the anticipated value.

This article will explore the practical applications and use cases of AI in proptech and real estate, potential adoption challenges, and ways to solve them.

Top Use Cases of AI in PropTech and Real Estate

McKinsey highlights that real estate companies integrating AI into their operations have gained significant benefits, including a more than 10% increase in net operating income. This enhancement stems from deploying more efficient operating models, enhancing customer experiences, and generating new revenue streams.

Let’s look at use cases of AI in real estate that could benefit proptech product companies, real estate financial companies, and residential and commercial real estate organizations.

Property management operations in commercial and residential real estate

AI and generative AI, in particular, can significantly enhance property management operations by automating routine tasks, performing data analysis, and improving tenant experiences. Recent studies indicate that organizations integrating AI into their property management workflows have higher expectations for net operating income growth than those not adopting AI. Specifically, 69% of AI users anticipate increased net operating income in 2024.

This is no surprise since AI has the power to minimize human risks, improve owner/renter satisfaction, and help property managers use data more efficiently.

Benefits of using AI in property management operations
Benefits of using AI in property management operations

Here, we’ve gathered the top 6 use cases of AI in real estate management and illustrated how this technology can augment the capabilities of property managers in commercial and residential sectors.

Top use cases of AI in property management operations
Top use cases of AI in property management operations

AI chatbots, LLMs, and virtual assistants for tenant support

The Live Chat Benchmark Report from 2022 highlights a significant trend: teams with 26 or more agents saw an astounding 138% increase in chats per agent. This suggests a rise in overall inquiry volumes, which property managers find challenging to cope with.

Adopting generative AI-powered assistants or large language model (LLM) solutions can minimize support staffing needs and offer multilingual assistance in managing tenant inquiries, service requests, and complaints around the clock. When more complex questions are asked, a custom LLM solution can flag them for a property manager to answer.

Automation of lease agreements and transactions

The global real estate market relies heavily on legal agreements, particularly leases, which support numerous transactions. Accuracy of these transactions is a must to adhere to Financial Reporting Standards like FASB ASC 842, IASB IFRS 16, and GASB 87.

Custom LLM solutions or generative AI tools can take in all necessary data about tenants, properties, and market conditions to draft agreements autonomously. This automation saves time, reduces errors, and ensures compliance with legal regulations, thereby effectively accelerating the leasing process.

Intelligent lease management and documentation

AI-powered applications can manage lease terms, track renewals, and automate rent collection. They efficiently monitor important dates and can send automated reminders to landlords and tenants. This, in turn, improves operational efficiency and reduces administrative burden.

Integrating an AI-enabled property leasing and maintenance platform allowed the US-based real estate investment trust Equity Residential to effectively handle 84% of inbound electronic leads. It helped the company reduce labor hours by approximately 7,500 per month and gain an additional US$15 million in net operating income.

AI-powered tenant screening

Empowering your property management with AI-powered tenant screening automates background checks, credit assessments, and bankruptcy inquiries for prospective tenants. These tools streamline the gathering and preparation of information, providing property managers with detailed insights to support decision-making.

However, to avoid potential biases inherent in AI-powered systems, real estate agents should also consider individual circumstances and make the final decision on tenant admission.

Tenant behavior analysis

In commercial real estate, understanding tenant behavior, such as space usage patterns, visitor traffic, popular areas, and peak usage times, is crucial for optimizing their experience and identifying opportunities for cross-selling and upselling. This data allows businesses to customize their offerings to meet tenant preferences and enhance overall satisfaction.

For instance, companies can collect such information using smart sensors installed on turnstiles. These sensors track foot traffic, occupancy rates, and movement patterns, providing real-time analytics that can help property managers optimize layouts, reducing unused space and tenant amenities.

AI-powered tenant behavior analysis
AI-powered tenant behavior analysis

Intelligent data processing

Integrating AI/ML-powered tools into real estate operations can streamline the handling of diverse agreements, some of which can be lengthy and complex, often exceeding 100 pages. Large Language Models, in particular, can extract the required information from vast text-based documents such as property listings, lease agreements, and sales contracts and significantly mitigate the time, resources, and potential errors associated with manual data entry.


The potential benefits of AI in property management operations extend beyond current applications, with the adoption of AI technology just beginning to accelerate. According to the 2024 Appfolio benchmark report, only 23% of property management professionals currently use AI, while an additional 30% are planning to adopt it in the near future.

As AI adoption grows, property management firms that embrace these technologies can gain a competitive edge. This presents a ripe opportunity for proptech companies to innovate and collaborate with industry stakeholders to shape the future of property management through AI-driven solutions.

Property transactions (purchase, selling, and rent)

AI technologies can reshape every facet of property transactions, from initial property search and valuation to closing deals and managing ongoing operations. Here are AI’s most common use cases in property purchase, selling, and rent operations.

Top use cases of AI in property purchase, selling, and renting
Top use cases of AI in property purchase, selling, and renting

AI/ML-powered residential search and listings

A search and listing engine that uses generative AI, integrated with MLS providers for comprehensive and real-time data, can significantly enhance the property search experience for buyers and renters. This technology improves the speed at which users identify suitable properties and expands the market reach for sellers by ensuring their listings reach more potential buyers.

For instance, Zillow, a leading real estate marketplace in the US, introduced an AI-powered natural language search feature last year. This feature allows users to bypass traditional filters and search for homes using queries like “$800K home in Florida with a pool” or “a forest house with two bedrooms.” By processing these queries and scanning millions of listing details, the AI delivers relevant results tailored to user preferences. Furthermore, the system continually learns from user interactions to refine its machine-learning models.

Automated property valuation

Valuation has the lowest rate of technology innovation and adoption amongst property subsectors. Given that it is administratively heavy, it has a huge potential for automation and AI usage. AI can analyze more data points, including local home prices, school quality, and job market data, and reduce report production costs. The Royal Institution of Chartered Surveyors (RICS) found that AI has the potential to reduce the time required for property valuations and significantly increase the accuracy of reports.

Personalized real estate brokerages

Brokers play a crucial role in facilitating transactions between buyers and sellers of real estate properties, yet the traditional model can be labor-intensive and time-consuming. Using AI-powered chatbots to automate services provided by brokers and brokerages can significantly enhance efficiency and customer satisfaction. In fact, 75% of America’s leading real estate brokerages already use the technology and almost 80% report that their agents have adopted AI tools.

AI-generated property and listing descriptions

Property and listing description writing is a longstanding and widely adopted use case for generative AI in real estate. Generative AI tools can write and optimize property listings by incorporating key features, amenities, and current market trends. This automation can considerably save agents’ time and enhance the search engine optimization of online listings, making them more discoverable for potential buyers and renters.

Virtual property tours and staging

Unlike traditional staging methods that rely on physical furniture and decorations, AI virtual staging operates entirely digitally. This approach significantly reduces costs while effectively showcasing the potential of empty spaces. For instance, AI virtual staging tools can remove unwanted items, place stylish furniture, and redecorate rooms or entire properties. Advanced AI algorithms also ensure that the digitally added elements appear realistic and visually appealing to potential buyers or renters.

Example of AI-powered virtual property staging
Example of AI-powered virtual property staging

This feature allows potential tenants to explore properties remotely and better understand their layout and potential before scheduling physical visits.

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Kateryna Ilnytska | Business Development Manager

Property finance and investment

Traditionally, deciding on what to invest in, underwriting mortgages, managing insurance policies, and conducting other property financial operations requires significant time and effort. AI can be a powerful tool and take over a lion’s share of these tasks.

Here are several use cases of how AI can be used in property finance and investment management.

Top use cases of AI in property finance
Top use cases of AI in property finance

Real estate financial modeling (REFM)

AI-powered REFM tools can pull information from third-party databases and open sources to research neighborhood statistics, property features, and historical data. Thanks to predictive analytics, these tools can assess how investment hypotheses have performed in the past and determine an investment’s potential profitability.

Additionally, these tools can analyze a list of properties for sale to identify and prioritize specific assets worth manual investigation. Such an approach minimizes uncertainty and risks associated with property investments, allowing investors to allocate their resources more effectively and maximize their returns.

Intelligent mortgage application and underwriting

AI-driven platforms can streamline the mortgage application and underwriting process by analyzing applicant data, credit history, and property information. This can speed up loan approvals, reduce processing costs, and improve the accuracy of risk assessments, providing human agents with all the necessary data to make decisions. McKinsey predicts that by 2030, AI, namely deep learning models, will help automate most of the underwriting process, so it will take just a few seconds.

No surprise, as even today AI-powered systems can quickly assess an applicant’s creditworthiness, predict their ability to repay the loan, and based on that offer personalized mortgage options. This, in turn, reduces the time it takes to secure a mortgage, resulting in better experience for borrowers.

Automated renters’ and homeowners’ insurance

The insurance sector also benefits from AI and automation, particularly in the domain of renters’ and homeowners’ insurance. ML models and AI-driven bots can automate the entire insurance lifecycle, from acquisition and underwriting to policy administration and claims management. AI systems can assess risk more accurately, recommend appropriate coverage, and streamline policy issuance. This helps enhance customer experience, reduce the time and cost of policy issuance, and improve claims accuracy and efficiency.

The KPMG 2023 Insurance CEO Outlook reveals a high level of trust in AI, with 58% of insurance CEOs confident they will see returns on investment within five years.

Automated due diligence

AI-powered tools can automatically gather and analyze vast amounts of data from multiple sources, such as public records, financial statements, and property listings. This automation saves time and reduces the risk of human error in data collection and analysis. For example, AI can quickly identify discrepancies in property records, flagging potential issues for further investigation.


While the top use cases of AI in proptech and real estate demonstrate this technology’s transformative potential, the industry is still not fully ready for AI transformation. Let’s learn what hinders it.

Challenges of AI Adoption in Real Estate and Proptech

Despite the growing adoption of proptech solutions, the real estate industry still struggles with outdated software, poorly structured data, and high resistance to change. Let’s explore the major challenges real estate companies may face when planning to integrate AI into their operations and ways to solve them.

Challenges of AI adoption in real estate and proptech
Challenges of AI adoption in real estate and proptech

Legacy software and infrastructure

The real estate industry is swamped with technical debt and outdated software, which hinders the adoption of AI and other emerging technologies. According to the Deloitte 2024 Commercial Real Estate Outlook, only 13% of real estate companies can access real-time business intelligence and analytics. So what about the rest?

The report states that 61% of real estate companies still rely on legacy systems, with only half of these companies trying to pursue modernization. At the same time, there is significant potential for automation, as 60% of companies still use spreadsheets for reporting, 51% for property valuation and cash flow analysis, and 45% for budgeting and forecasting.

To adopt AI, these companies first need to modernize their existing legacy systems and move to the cloud (if applicable) to support the integration of advanced technologies.

Poor data quality and availability

AI systems require vast amounts of high-quality data to function effectively. In real estate, data, particularly historical data, can often be fragmented and difficult to obtain. A Deloitte survey of over 750 CRE professionals identified the lack of quality data as one of the top four challenges in making timely decisions. The primary reason for this lies in inconsistent data collection methods.

Many real estate companies still rely on manual data collection and management, which leads to incomplete and inaccurate datasets. This manual approach creates frustration and hampers the ability to get insights quickly.

To overcome this, companies must adopt standardized and automated data collection processes, ensuring the data is accurate, comprehensive, and readily accessible for AI applications. For example, implementing centralized data management systems can help streamline data collection across various sources.

Lack of technical expertise

Despite significant layoffs among tech companies and declining tech-related job postings, the demand for advanced skills in AI and gen AI implementation increased, particularly in the real estate sector. The number of job postings outperforms the number of qualified candidates on the market, holding AI’s potential back.

To overcome this tech talent gap and drive efficiency, proptech and real estate companies can outsource AI /ML and generative AI development services or hire a dedicated team abroad. Deloitte states that 61% of real estate companies are planning to use outsourcing to gain technological capabilities, streamline processes, and make operations more agile and resilient.

Guaranteeing compliance with laws and regulations

Real estate is a highly regulated industry, and AI applications must comply with various laws and regulations regarding data privacy, financial transactions, and property management. These include the US Executive Order on AI and the EU AI Act, which the European Parliament recently approved. At the same time, China, Canada, and Australia are actively advancing their own AI legislative efforts.

Real estate and proptech companies must ensure their AI solutions comply with all relevant laws. Given the differences in regulations across regions, this can be a daunting task. The compliance involves rigorous data protection measures, transparent financial practices, and adherence to property management standards.

To overcome this challenge, companies require hiring a dedicated team or external advisors who can monitor regulatory developments and implement necessary adjustments to ensure compliance.

Ethical considerations

Adopting AI in real estate brings about significant ethical considerations. One major challenge is ensuring that AI systems do not make biased decisions, which can lead to unfair treatment of certain groups and potential legal and ethical issues.

Relying on seemingly objective tools may not provide enough context to arrive at an appropriate decision, for instance, in tenant admission determinations. The Department of Housing and Urban Development (HUD) addressed concerns about the fair use of AI in tenant selection in April 2024, emphasizing the importance of human oversight to supplement AI-driven insights with contextual understanding.

Resistance to change

Many real estate companies realize the potential that integrating AI and generative AI models can bring to their business. However, they may still face resistance from employees who are accustomed to using spreadsheets and other traditional methods.

To overcome this resistance, companies need to invest in change management and employee training. Helping teams understand that AI integration will assist rather than replace human roles can foster a more receptive attitude toward new technologies. Establishing a clear vision and strategy for AI integration, emphasizing its role in staying competitive, and demonstrating how AI can enhance work, reduce mundane tasks, and provide valuable insights can also help.

In a Nutshell

Generative AI can revolutionize real estate, but the industry must first address its own challenges to reap the benefits. Legacy software solutions, manual operations, and resistance to change hinder AI adoption, putting companies at risk of losing users and falling behind the competition.

Adopting AI can help real estate and proptech companies increase operational efficiency, use data more effectively, automate processes, and reduce the risk of human errors. Many companies that have already integrated AI into their processes have seen an increase in net operating income.

As technology finds more use cases in real estate and becomes more deeply integrated into the industry, the need to change and adopt AI becomes even more critical. However, the lack of tech talent experienced in developing AI-powered custom solutions can lead to delays and increased costs in AI adoption, further hindering progress.

Finding a reliable software development partner is essential for successful AI integration. With eight years of experience in real estate proptech software development, Leobit has built custom proptech solutions catering to over 1.5 million users and facilitating $1.4 billion in real estate transactions. The 2024 Global Business Tech Awards recognized our company as the Best Property Tech Company of the Year, and Clutch named us the Top Real Estate Software Developer of 2023.

Leobit's experience and awards in real estate and proptech
Leobit’s experience and awards in real estate and proptech

When it comes to AI adoption, Leobit has already transformed our internal processes by developing and integrating custom corporate LLM solutions. We have hands-on experience using different generative models and AI API tools and can help you design, train, and integrate custom AI-powered solutions in your real estate/proptech software solution. Contact us, and we’ll gladly consult you deeper on the topic.

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Artem Matsa | Business Development Director