leewayhertz.com-AI in real estate Impacting the dynamics of the modern property market-1

1. www.leewayhertz.com/ai-in-real-estate/ AI in real estate: Impacting the dynamics of the modern property market Artificial Intelligence (AI) has garnered widespread attention across industries, and real estate is no exception. The real estate market is on the cusp of a significant shift, with AI projected to reach a substantial valuation of US $ 1335.89 Bn by 2029, exhibiting a remarkable CAGR of 35% during the forecast period. This dynamic growth underscores AI’s potential to redefine the real estate sector, ushering in a new era of innovation and efficiency. AI-powered solutions are gradually transforming the real estate industry by simplifying and expediting complex processes, ultimately boosting work efficiency across various roles, including sellers, brokers, asset managers, and investors. Through automation and data-driven insights, these innovations empower real estate professionals to navigate the complexities of their field with greater ease and productivity. Moreover, the efficiency gains catalyzed by AI hold the promise of significant reductions in costs associated with real estate transactions. This article delves into the multifaceted role of AI within the real estate sector, serving as a compass navigating through its intricate terrain. Further, we will explore the various AI techniques real estate businesses use and real-world examples of real estate organizations using AI. We will also cover the future implications of artificial intelligence in real estate. Traditional real estate systems and the challenges they faced How can AI address the shortcomings of legacy real estate systems? AI use cases in the real estate industry Advanced AI techniques used in the real estate industry Examples of real estate organizations using AI AI in real estate: Future trends and scope Traditional real estate systems and the challenges they faced 1/14

2. The real estate industry has always been a significant pillar of the global economy, connecting buyers and sellers in the pursuit of properties for residential, commercial, or investment purposes. Traditionally, the process of buying, selling, and managing real estate has been largely manual, relying on human expertise and effort. However, in recent years, the integration of Artificial Intelligence (AI) has significantly impacted the real estate sector, reshaping how properties are searched, evaluated, and managed. Understanding traditional real estate systems The real estate industry is a complex ecosystem comprising various stakeholders, including buyers, sellers, agents, developers, management companies, and investors. It involves a multitude of processes, from property search and valuation to marketing, sales, and property management. The industry’s success hinges on factors such as market trends, property demand, economic conditions, and consumer behavior. Traditional real estate systems have long been the backbone of the industry, providing the framework for property transactions, marketing, and management. These systems, often manual and paper-based, have served the industry for decades. However, with advancements in technology and the rise of AI, the limitations of traditional systems have become more apparent, prompting the need for innovative solutions. Understanding the workings of traditional real estate systems is essential to appreciate the potential benefits that AI integration can bring to the industry. Manual processes and paperwork Historically, real estate transactions involved significant manual work and paperwork. From filling out property listings to managing contracts and documentation, real estate professionals spent considerable time on administrative tasks. This manual approach consumed valuable time and introduced the risk of errors, leading to inefficiencies and delays in property transactions. 2/14

3. Limited data analysis capabilities Traditional real estate systems often lacked sophisticated data analysis capabilities. Real estate professionals had to rely on their experience and intuition to make decisions about property valuations, market trends, and investment opportunities. Limited data analysis meant that valuable insights from large datasets were not effectively utilized, leading to suboptimal decision-making. Time-consuming property search and valuations For buyers, finding the right property involves physically visiting multiple properties or browsing through listings manually. Property valuations were often subjective and conducted by human appraisers, leading to variations in property values. These time-consuming and subjective processes were barriers to efficiency in the real estate market. Lack of personalization Real estate systems often lacked personalization in property searches and recommendations. Buyers and investors had to rely on generic listings and recommendations, making it challenging to find properties that precisely matched their preferences and needs. Inefficient property management Property management was labor-intensive and involved juggling various tasks, such as tenant communication, rent collection, and maintenance. Traditional systems made it challenging to streamline property management processes, leading to potential inefficiencies and increased costs for property owners and managers. Data silos and communication gaps In many cases, different stakeholders in the real estate industry worked with fragmented data systems that were not integrated. This led to communication gaps and a lack of transparency between buyers, sellers, agents, and property management companies, causing delays and misunderstandings in property transactions. Dependence on local expertise Traditional real estate systems heavily relied on local expertise and knowledge. While local expertise is invaluable, it limits the scope for broader market insights and trends, potentially limiting growth opportunities for investors and businesses. As the real estate industry evolves, these challenges present opportunities for AI integration to address these shortcomings and unlock the market’s potential. AI-powered systems can streamline processes, enhance data analysis capabilities, provide personalized experiences, and improve overall efficiency in the real estate industry. By understanding the limitations of traditional systems, real estate professionals can better appreciate AI’s value and embrace the opportunities for innovation and growth. 3/14

4. How can AI address the shortcomings of legacy real estate systems? AI integration can effectively address the challenges posed by traditional real estate systems in several ways: Automation and digitalization: AI reduces the administrative burden on real estate professionals by automating manual processes and digitizing paperwork. This streamlines transactions, minimizes errors and accelerates property deals. Advanced data analytics: AI can analyze vast datasets quickly and accurately, providing valuable insights into property valuations, market trends, and investment opportunities. Real estate decisions become more data-driven and informed. Enhanced property search: AI-powered tools offer efficient property search mechanisms, saving buyers and investors time and effort. Machine learning algorithms can suggest properties that align with specific preferences and requirements, improving the property discovery process. Personalization: AI-driven platforms can personalize property recommendations, tailoring listings to individual preferences. This ensures that clients find properties that match their unique needs, enhancing their overall experience. Efficient property management: AI can optimize property management by automating tasks like rent collection, maintenance scheduling, and tenant communication. This leads to cost savings and smoother property operations. Data integration and communication: AI systems can integrate data from various sources, fostering better communication and transparency among stakeholders. This reduces misunderstandings, delays, and data silos within the real estate ecosystem. Wider market insights: AI expands market insights beyond local expertise. It can analyze data from broader geographical areas and identify emerging trends, allowing investors to explore new growth opportunities. AI integration can redefine traditional real estate systems by automating tasks, improving data analysis, personalizing experiences, and enhancing overall efficiency. By embracing AI technology, the real estate industry can overcome its historical challenges and unlock new possibilities for growth and innovation. AI use cases in the real estate industry AI has significantly impacted the real estate industry, bringing forth a range of use cases that have streamlined processes, improved decision-making, and enhanced customer experiences. Below are some detailed AI use cases in the real estate industry: 4/14

5. Property search and recommendations AI-powered property search platforms have redefined the way potential buyers navigate the real estate market. These platforms harness the power of machine learning algorithms to meticulously analyze an extensive array of data, encompassing property listings, user preferences, historical search behaviors, and location-based insights. Through the analytical capability, these platforms are adept at delivering highly personalized property recommendations tailored to each potential buyer’s unique preferences, budget constraints, desired locations, and other essential criteria. This expedites the property search process and significantly enhances the overall user experience. Furthermore, the integration of AI proves especially advantageous for large estate enterprises with an extensive inventory of properties for sale. Such enterprises can efficiently refine their target audience and elevate the perceived value of their offerings. AI’s capabilities extend to generating custom property listings aligned with a client’s historical viewing patterns and preferences, thereby saving valuable time. Beyond this, AI employs sophisticated profiling strategies to present optimal property options to first-time customers, leveraging past demographic insights and similar clients’ success patterns. This automated approach far surpasses the efficiency of manual searches through ever-expanding real estate databases. Complementing these features, AI-driven real estate search engines prioritize the customer experience by providing intuitive interfaces and streamlined search processes. This commitment to user convenience 5/14

6. ensures that users receive relevant and exceptional property selections. Moreover, AI systems continuously enhance their recommendations by integrating user feedback and conducting thorough behavioral evaluations. In doing so, they guarantee that users consistently receive property recommendations that are not only pertinent to their evolving preferences but also of exceptional quality. Predictive analytics for investment and market analysis In the realm of real estate, predictive analytics has emerged as a potent application of artificial intelligence. Serving as the foundation for property value estimations, predictive analytics eliminates the complexities of assessing market worth. This approach delves into historical sales data, market trends, economic indicators, and relevant factors to make accurate forecasts by leveraging AI algorithms. The real estate industry benefits from AI’s ability to predict market trends, facilitating informed decisions for property investments, market identification, and pricing strategies. AI-powered predictive analytics extends its influence beyond valuation. Precise forecasts are generated by considering factors like population growth, job prospects, infrastructure development, and investor sentiment. This aids investors in identifying regions with significant growth potential, guiding them toward sound investment decisions. The integration of predictive analytics empowers real estate stakeholders to strategically navigate the dynamic market landscape, ensuring optimal investment choices in areas poised for prosperity. Virtual assistants and chatbots The integration of AI-powered virtual assistants and chatbots has ushered in a new era of convenience and efficiency in the real estate landscape. By harnessing Natural Language Processing (NLP) techniques, these intelligent computer systems can interpret and comprehend user queries expressed in natural language. This breakthrough enables users to engage in conversational searches, make property inquiries and receive tailored recommendations effortlessly. One of the key benefits of real estate chatbots and virtual assistants lies in their ability to provide in-depth information about residential properties. They can furnish users with detailed specifications, amenities, precise locations, neighboring educational institutions, and available transportation options. This reservoir of information eliminates the need for tedious searches through physical documents or constant visits to real estate agents for clarifications. Users can now pose specific questions and receive immediate, accurate, and updated responses from these AI-driven entities. Beyond property details, real estate virtual assistants serve as valuable sources of market insights. They provide customers with comprehensive market trends data, enabling more informed decision-making. These AI-powered assistants can analyze recent sales, marketplace conditions, and analogous properties to provide customers with a deeper understanding of market patterns, potential investment opportunities, and emerging areas for development. Chatbots empower users to navigate the real estate landscape by presenting data-backed insights with a heightened sense of confidence. In a world driven by convenience and information, the integration of virtual assistants and chatbots enhanced by NLP capabilities significantly impacts real estate interactions. The integration of AI in the real estate industry enhances the overall customer experience, redefining how property inquiries are made, property details are accessed, and strategic decisions are formulated. 6/14

7. Automated property valuation Automated property valuation refers to the process of determining the monetary worth of real estate using AI algorithms. It holds great significance for a range of stakeholders, including consumers, sellers, lenders, and investors. These AI algorithms produce precise and unbiased property valuations by harnessing historical data, local market conditions, and various property attributes. This eliminates inherent subjective biases that can skew valuation outcomes and lead to issues like overvaluation or undervaluation. AI-based property assessment models rely on advanced machine learning techniques to continually refine their valuation algorithms. These models consider many influential factors, such as the property’s geographical location, size, amenities, recent comparable sales prices, neighborhood intricacies, and the prevailing state of the real estate market. This comprehensive approach is aimed at reducing the impact of subjective influences and inaccuracies, which in turn significantly enhances the accuracy of property valuations. Automated property management Effective property management stands as a paramount challenge in the real estate industry, particularly for those overseeing multiple rental properties and multifamily residences. However, the integration of AI has introduced a game-changing solution, redefining the way property management tasks are executed and enhancing the overall landscape of real estate operations. The adoption of AI technologies in property management holds the potential to automate an array of tasks, thereby alleviating the burdens property managers face. This automation lightens their responsibilities and significantly reduces the chances of human errors that can lead to issues such as accounting discrepancies, tardy rental payments, and tenant disputes. By harnessing the power of AI, the real estate industry can take a significant stride toward error-free, efficient property management. One remarkable aspect of AI-powered technologies is their ability to provide clients with immersive experiences that transcend traditional boundaries. Prospective tenants can virtually explore properties through 3D views, enabling them to gain an in-depth understanding of homes without the need for physical visits. With features like zoom-in tools, spoken notes, and panoramic views, AI-driven innovations grant clients an enriched perspective, enhancing their decision-making process and overall engagement. Furthermore, AI-powered property management platforms assume a multifaceted role in overseeing real estate properties. From seamlessly collecting rent and scheduling maintenance tasks to meticulously screening potential tenants and overseeing lease administration, these platforms serve as comprehensive tools that optimize various facets of property management. Property managers can focus on more strategic endeavors by entrusting repetitive and time-consuming tasks to AI, ensuring efficient operations and elevated customer satisfaction. Portfolio optimization in real estate investment AI has profoundly impacted portfolio optimization in real estate by enhancing decision-making processes and maximizing returns. Traditional methods often rely on historical data and simplistic models, leading to 7/14

8. suboptimal results. AI brings a data-driven approach, utilizing machine learning algorithms to analyze vast datasets of property information, market trends, economic indicators, and more. AI can forecast property values, rental income, and market fluctuations through predictive analytics, enabling investors to make informed choices. ML algorithms can identify patterns and correlations in data, assisting in identifying undervalued properties or areas with growth potential. Furthermore, AI- powered risk assessment models evaluate various factors, such as market volatility and economic indicators, to provide a comprehensive risk profile for each investment. This aids in creating diversified portfolios that balance risk and return. Real-time data processing allows for dynamic portfolio adjustments, ensuring alignment with market changes. Natural language processing facilitates the analysis of unstructured data from news articles, social media, and industry reports, providing additional insights. Automated lead generation AI has redefined lead generation in the real estate sector by streamlining processes and enhancing conversion rates. Traditional methods often involve manual sorting and analysis of leads, resulting in inefficiencies and missed opportunities. The real estate industry leverages machine learning to automate and optimize lead generation. AI algorithms analyze vast datasets to identify potential leads based on factors such as demographics, online behavior, and property preferences. This enables agents to target prospects more accurately and tailor their marketing efforts. Through predictive analytics, AI can predict the likelihood of a lead converting into a customer, enabling agents to prioritize high-potential leads and allocate resources effectively. Implementing AI across these use cases has led to increased efficiency, reduced human errors, improved customer satisfaction, and more data-driven decision-making in the real estate industry. As AI technology advances, it is expected to drive even greater innovation in the sector. Advanced AI techniques used in the real estate industry Advanced AI techniques have brought significant changes to the real estate industry, enabling more accurate predictions, personalized experiences, and efficient processes. Here are some of the advanced AI techniques used in the real estate industry: Automated Valuation Models (AVMs) Automated Valuation Models utilize regression algorithms and historical sales data to estimate property values. To provide accurate and data-driven property valuations, AVMs consider various factors, such as property features, location, comparable sales, and market trends. By removing human bias and providing real-time valuations, AVMs expedite the property appraisal process and improve pricing accuracy for sellers and buyers alike. Collaborative filtering models Collaborative filtering models are used in real estate recommendation systems. These models analyze user behavior, preferences, and historical interactions to offer personalized property recommendations. 8/14

9. By identifying patterns and similarities between users, collaborative filtering models match potential buyers with properties that align with their preferences, increasing the chances of finding their ideal property. Natural Language Processing (NLP) for property search NLP enables real estate platforms to understand and interpret user queries in natural language. NLP- powered search engines can provide more relevant and precise property search results by analyzing text data from property descriptions, user inquiries, and reviews. This enhances the user experience and helps potential buyers find properties that match their preferences more effectively. Computer vision for property valuation Computer vision algorithms analyze visual data, such as videos or images of properties, to extract features and assess property conditions. These AI techniques can estimate property values based on visual attributes like architectural style, interior finishes, and overall aesthetics. Computer vision also aids in detecting property defects and providing valuable insights for property valuations. Deep learning for market analysis Deep learning models, particularly neural networks, play a crucial role in complex market analysis and trend prediction. These advanced algorithms excel at recognizing patterns and generating precise predictions related to property market trends. They achieve this by processing extensive datasets, which encompass historical property sales, market trends, and economic indicators. The insights derived from deep learning models empower real estate professionals to make well-informed decisions regarding property investments and pricing strategies. Reinforcement learning for property management Reinforcement learning is utilized to optimize property management strategies. AI systems can learn from past actions and adjust property management decisions based on outcomes. For example, an AI- powered property management system can optimize rental pricing based on tenant responses and maximize revenue over time. Generative Adversarial Networks (GANs) for property generation GANs are used to generate synthetic property images and floor plans. These AI models can create realistic property listings, allowing real estate developers to showcase potential projects or visualize design changes without constructing physical prototypes. GANs also facilitate virtual staging, making vacant properties more appealing to potential buyers. Transfer learning for limited data scenarios Transfer learning techniques allow AI models to leverage knowledge gained from one task or dataset and apply it to another related task with limited data. In real estate, this helps when training AI models on specific property types or in markets with less data availability. Ensemble learning for robust predictions 9/14

10. Ensemble learning combines predictions from multiple AI models to make more robust and accurate forecasts. In the real estate industry, ensemble models are used for property valuation and market trend predictions to reduce the impact of individual model biases and improve overall prediction accuracy. Recommender systems for personalized property marketing Recommender systems use machine learning algorithms to predict users’ preferences and recommend relevant properties to potential buyers. By analyzing user behavior, historical search data, and demographic information, these systems offer personalized marketing strategies to connect buyers with properties that meet their specific needs. Cluster analysis for market segmentation Cluster analysis techniques group properties and customers into segments based on shared characteristics. AI-powered cluster analysis helps identify distinct market segments with specific preferences, allowing real estate professionals to effectively tailor marketing strategies for each group. Sentiment analysis for property market trends Sentiment analysis uses NLP and machine learning to analyze textual information like social media posts, news articles and online reviews to gauge market sentiment. By understanding public opinions and sentiments, real estate professionals can make data-driven decisions and predict shifts in market trends. Advanced AI techniques in the real estate industry provide valuable tools for market analysis, property valuation, personalized marketing, and property management. By harnessing the power of these AI technologies, real estate professionals can attain a competitive edge, enhance customer experiences, and make data-driven decisions for better growth and efficiency. Examples of real estate organizations using AI Real estate companies have embraced various technologies, including AI and ML, to impact the industry significantly. These companies are redefining the property buying, selling, and investing process by harnessing AI in real estate. Here are some top examples of real estate companies that use AI and ML to attract investors: Zillow Zillow, a trusted name in the real estate industry, employs AI to analyze digital photos and relevant metrics to generate free property value estimates, known as Zestimates. Using neural networks trained on vast datasets of millions of photos and corresponding home values, Zillow’s AI “reads” photos in new property listings to extract information. The AI-driven estimates project property values with a national median error rate of just 2.4%, contributing to highly accurate predictions. Redfin Redfin is a technology-powered real estate broker, lender, instant home-buyer (iBuyer), title insurer, and renovations company. It utilizes AI to provide automated property valuations, combining AI-driven data 10/14

11. analysis with human expertise to offer accurate valuations to realtors and clients. Trulia Trulia, a prominent real estate marketplace, uses AI to provide personalized property recommendations and enhance user experiences. By analyzing user behavior, search history, and preferences, this AI- powered platform offers tailored property suggestions that match individual preferences and interests, making property searching more efficient and enjoyable for users. Keyway Keyway is an AI-powered real estate investment manager that utilizes AI, data science, and other technologies to make well-informed choices and attain improved outcomes. The company collaborates with multifamily property owners and medical professionals in the healthcare sector, such as doctors, veterinarians, and dentists. Keyway offers service models like rent-to-own and transition-out, bringing innovative solutions to real estate investing. Skyline AI Skyline AI, a JLL company, offers a powerful tool for commercial real estate investors that combines human real estate expertise with AI’s ability to analyze vast amounts of data. The platform parses location, web data, and AI-generated data to inform investor decisions on when to buy and sell properties. Skyline AI has engineered a platform to expedite and expand the analysis of commercial real estate properties, ensuring swifter and more comprehensive evaluations. Skyline AI amplifies current knowledge and enhances performance across the investment analysis journey through data extraction from diverse sources and utilizing supervised and unsupervised machine learning models. HouseCanary HouseCanary employs advanced machine learning-powered technology to offer unprecedented clarity and resources for home seekers. Their comprehensive collection of data, updates, and tailored searches empowers customers to make informed decisions in the residential real estate market, ensuring the right property selection. They combine AI and image recognition, extracting actionable insights from a vast sea of real estate information. Entera Entera is a leading example of AI in real estate companies, serving as a residential real estate investing platform. The platform leverages AI to provide automated processes and intelligent analytics for discovering, purchasing, and managing single-family homes. With operations across various US markets, Entera’s platform handles over 1,000 monthly transactions, streamlining the real estate investment process. Cherre Cherre is a leading real estate data and insight company that connects decision-makers to accurate property and market information. Enabling customers to swiftly and accurately evaluate opportunities and 11/14

12. trends, Cherre saves millions of dollars by replacing labor-intensive data collection and analytics processes with their advanced, automated technology and data solutions. These real estate companies demonstrate the power of AI and ML in enhancing decision-making, optimizing property valuations, and providing personalized experiences to attract more investors and reshape the real estate industry. As technology evolves, we can foresee the rise of increasingly inventive AI-driven solutions within the real estate sector. AI in real estate: Future trends and scope AI’s integration into the real estate industry has already shown impactful results, reshaping traditional processes and enhancing customer experiences. Looking to the future, AI has several emerging trends and vast potential to shape the real estate landscape even further. Here are some future trends and the scope of AI in real estate: Image and video recognition AI algorithms are rapidly advancing in image and video recognition. In the future, these algorithms can analyze property images and videos to extract relevant information such as property features, room dimensions, and architectural details. This technology will enhance property listings with more detailed information and provide potential buyers with immersive virtual property tours, offering a better understanding of the property before a physical visit. Smart contracts and blockchain integration AI’s integration with blockchain technology will enable the implementation of smart contracts in real estate transactions. Smart contracts are self-executing agreements with the terms and conditions written directly into the code. This technology will redefine the way property transactions are conducted, making them more secure, transparent, and efficient. Smart contracts will reduce the need for intermediaries, minimize paperwork, and enhance trust between buyers and sellers. Smart cities and sustainability AI is poised to substantially impact the development of smart cities and sustainable real estate ecosystems. AI-powered systems will optimize energy consumption, reduce environmental impact, and improve urban planning and infrastructure management. By analyzing data from IoT devices and sensors deployed across cities, AI will assist in creating energy-efficient buildings, integrated transportation systems, and intelligent waste management solutions. Real estate developments within smart cities will prioritize sustainability, offering eco-friendly living spaces with advanced energy management systems. Hyper-personalization of customer experiences In the future, AI will drive hyper-personalization in customer experiences within the real estate industry. AI algorithms will analyze vast amounts of data, including past interactions, preferences, and behaviors, to deliver highly personalized property recommendations and marketing campaigns. Real estate professionals will have the capability to provide tailored services that precisely match the unique needs and preferences of each buyer and seller, enhancing customer satisfaction and loyalty. 12/14

13. AI-driven property management AI will continue to greatly impact property management by automating routine tasks, optimizing maintenance schedules, and improving tenant experiences. AI-powered property management systems will analyze data from various sources to predict maintenance requirements, reduce downtime, and ensure the efficient operation of properties. Additionally, AI-driven tenant management systems will enhance tenant communication, facilitate rent collection, and provide instant support, streamlining property management operations. Data-driven market insights As AI becomes more sophisticated, real estate professionals will have access to data-driven market insights in real-time. AI algorithms will process vast amounts of data from multiple sources to identify emerging market trends, predict property demand, and assess investment opportunities. Real estate experts can use this data-driven approach to enhance their decision-making acumen and proactively navigate market fluctuations. Voice-activated search and assistance With advancements in NLP, voice-activated AI assistants will become more prevalent in real estate. Potential buyers and sellers will have the option to utilize voice commands for property searches, listing inquiries, and obtaining property-related details, thereby enhancing the convenience of the property search process. The future of AI applications in real estate holds immense innovation potential. From image and video recognition to smart contracts and sustainable smart cities, AI will continue to redefine the real estate landscape. By leveraging AI’s capabilities, real estate professionals can offer personalized experiences, optimize operations, and make data-driven decisions, ultimately shaping a more efficient, sustainable, and customer-centric real estate market. Conclusion AI has emerged as a game-changer in the real estate industry, reshaping the way properties are bought, sold, and managed. From streamlining processes to enhancing decision-making, AI’s integration has catalyzed a paradigm shift, unlocking new possibilities and redefining the real estate landscape. Through predictive analytics, AI empowers real estate professionals to make data-driven decisions, accurately forecasting market trends and property demand. Buyers and sellers benefit from personalized experiences, with AI-driven platforms offering tailored property recommendations and immersive virtual tours. AI has also redefined property valuations, automating the process with increased accuracy and transparency. Real estate professionals and consumers receive real-time and data-driven property estimates through automated valuation models, ensuring fair and competitive pricing. The future of AI in real estate looks even more promising, with trends like smart cities and sustainability, voice-activated search, and blockchain integration set to redefine the industry. 13/14

14. However, with AI’s game-changing potential comes the need for responsible and ethical use. Ensuring data privacy, safeguarding against biases, and maintaining transparency will be critical as AI’s influence deepens in the real estate sector. As we look ahead, the fusion of AI with real estate will undoubtedly unlock unprecedented potential, making the dream of smarter, sustainable, and customer-centric properties a tangible reality. Real estate professionals and stakeholders who embrace AI’s capabilities will be at the forefront of this shift, shaping the industry’s dynamic and prosperous future. With AI as our ally, the future of real estate is limitless. Ready to redefine your real estate processes with AI? Contact LeewayHertz’s AI experts today and leverage advanced AI-powered solutions to elevate your real estate business to new heights. 14/14