Mary Burko

Mary Burko

Content Writer, Researcher

Investing in artificial intelligence in real estate is a trend to keep an eye on. Having doubts? The numbers tell the story: AI real estate startups raised $8 billion in the first half of 2022, while 70% of all real estate deals involved AI startups. In real estate, generative AI differs from our standard AI in many ways, but how exactly? Does it replace real estate professionals, or is it simply a tool to automate their processes? Let's dive into the article to get the answers to these questions.

What is Artificial Intelligence in Real Estate?

Using artificial intelligence (AI) in the real estate industry refers to using computers or machines to perform intelligent tasks, such as speech and image recognition. This is achieved by mimicking cognitive processes associated with human intelligence, including learning, reasoning, comprehension, and problem-solving.

The functioning of AI relies on 'neural networks,' which are computational models designed to replicate the processes of the human brain. In this context, each neuron in the network passes data to the next one, analogous to players passing a basketball to score a goal.

The Progression of AI in the Real Estate Sector

While some sources may assert that AI made its debut in real estate in 2018 with the first AI-backed transaction involving the $26 million purchase of two Philadelphia buildings, the reality is that AI's presence in real estate predates this milestone.

In the early 2000s, AI ushered in the era of FAQ chatbots, though they were less commonly used than today. The initial tech player to make waves in AI real estate tools was PropTrack, an Australian company that introduced an AI-powered property valuation tool in 2012.

Home research software and home value estimator tools based on artificial intelligence were developed in the following years. HomeSnap and Zillow led the way in presenting these innovations to the market in the years that followed.

The momentum of AI in real estate continued to grow. In 2014, Trulia introduced the first AI-powered lead generation tool for brokers, and by 2015, RealScout pioneered the development of home search tools for real estate agents. These tools were designed to help agents identify homes with the highest likelihood of quick sales and the potential for the highest return on investment.

Since 2022, the real estate sector has witnessed the rise of generative AI, exemplified by the introduction of a 3D modeling tool by TermSheet, which we will delve into later in this article. Noteworthy advancements include:

  • Image recognition models for intelligent property appraisal
  • AI-powered chatbots and virtual assistants
  • Virtual staging technology
  • Property description generators and more

Additionally, Predictive AI has made strides with the development of:

  • AI investment analysis tools
  • Underwriting tools and others

Looking ahead, the trajectory of AI development in real estate promises increased autonomy and creativity. Anticipate innovations such as a property management tool capable of autonomously making property-related decisions. This tool would handle communication, process maintenance requests, and manage financial operations independently from users.

Types of AI Models in Real Estate

We've identified two classifications of Artificial Intelligence in the real estate domain.

Based on Capabilities:

Narrow AI: These systems undertake straightforward tasks and can learn from experience. Think of them as tools to predict market trends, analyze property images, or answer queries like virtual assistants.

General AI: These systems act as versatile, intelligent machines capable of performing complex tasks and learning independently. Imagine machines executing tasks, comprehending emotions, and exhibiting creative thinking. For instance, envision an AI guide leading a virtual property tour and engaging in conversation like a natural person. General AI can revolutionize property searches by deeply understanding buyers' preferences, financial situations, and lifestyles to offer highly accurate property recommendations aligned with their needs. However, fully developed general AI in real estate has yet to be realized.

Super AI: This represents the superhero of AI, designed to outperform humans significantly. A potential application of Super AI in real estate would be autonomously managing entire portfolios of real estate investments making decisions based on various data sources. The majority of current AI applications in real estate are in the Narrow AI category, while General AI and Super AI remain aspirational technologies.

Based on Functionality:

Reactive Machines: These models follow pre-defined rules for processing data and cannot self-improve. Examples include basic chatbots for FAQs, property listing classification, property search filters, and mortgage affordability calculators.

Limited Memory: AI models capable of storing and accessing data from past experiences for a short duration. Applications include personalized property recommendations and chatbots offering customized investment strategies.

Theory of Mind: Artificial intelligence systems that are designed to anticipate the intentions and emotions of users and respond accordingly. While partially realized, examples include humanoid robots like Sophia from Hanson Robotics, which can recognize faces, express feelings, and communicate on predefined topics. In real estate, such AI could engage in conversation, understand user preferences, and assist in finding properties that align with their emotions and choices.

Self-Awareness: A conceptual idea, envisioning AI evolving to possess emotions and opinions. This advanced AI could serve as a virtual advisor in real estate, understanding user emotions and offering insights into property trends and preferences akin to a knowledgeable friend. However, it's important to note that this level of AI development has not yet been achieved.

AI: Generative AI vs Predictive AI

As previously discussed, AI is a broader concept with both Generative AI and Predictive AI serving as its subcategories.

Generative AI comprises models designed to generate new images, videos, text, and music by leveraging existing patterns and data. If you're not oblivious to recent technological advancements, you've likely encountered and possibly utilized ChatGPT—an example of generative AI that generates texts and engages in conversations with users. The following sections of this article delve into multiple types and examples of Generative AI, examining their applications in real estate.

On the other hand, Predictive AI involves models designed to make forecasts based on historical data provided to the system. In real estate, AI is integrated into tools that predict market trends, offering valuable insights, especially for investors, as well as forecasting property prices and mortgage affordability.

Given the significant impact of Generative AI on various industries, let's delve further into its applications as we explore its transformative role in the world.

Real Estate Generative AI Models

Beyond real estate, these advancements have extended their reach into diverse industries committed to pushing boundaries. It was the development of Long Short-Term Memory Networks by Sepp Hochreiter and Jurgen Schmidhuber in December 1997 that paved the way for the emergence of Generative AI versus Predictive AI models in the 2010s. Image2.png

Natural Language Processing Models (NLP) disassemble text into smaller components, such as phrases and words, scrutinize the connections among these elements regarding grammar and syntax, and ultimately produce responses akin to human language.

Conversational AI, a subset of Generative AI, relies on NLP to facilitate vibrant, real-time interactions between individuals and machines. Within the real estate domain, prominent instances of Conversational AI include chatbots (like STAN AI), virtual assistants (such as Diffe. rent), and text-based concierge services (like RoofAI).

Generative Adversarial Networks (GANs) represent a machine learning model comprising two components: a generator that fabricates new data and a discriminator that assesses its authenticity. This aspect remains noteworthy as machine learning models can retain historical data and enhance their performance autonomously.

Regarding their application in real estate, GANs, conceptualized by Ian Goodfellow in June 2014, predominantly find utility in generating images for architectural design and urban planning. Tools for generative design, such as InteriorAI, Dream Staging AI, and 3D modeling platforms like GANVerse 3D, are built upon the foundation of GAN technology.

Transformer-based models represent a significant advancement in NLP, incorporating 'self-attention' to assign varying levels of significance to words, enabling them to comprehend intricate contextual relationships.

Consider ChatGPT as an illustration. GPT-1 emerged in 2018, leveraging the transformer model and trained on ... books. Subsequently, GPT-2 arrived in 2019 and is capable of text generation. Compared to its predecessors, GPT-3, as we're familiar with it, can generate more expansive texts, translate content, conduct calculations, and even code.

In real estate, transformer-based models are the pinnacle in property description generation tools (such as ListingAI) and the analysis of leases and contracts (like Doma).

Deep reinforcement learning is the convergence of deep learning and reinforcement techniques. Deep learning involves multi-layered neural networks that discern intricate data patterns and process them. On the other hand, reinforcement learning instructs machines to make swift context-driven decisions in dynamic environments.

In real estate, although deep reinforcement learning remains relatively uncharted territory in tech development, it holds significant promise for the industry's future. Particularly in investment and real estate trading, where actions like 'buying' and 'selling' can be treated as reinforcement actions within the operational model, it presents substantial potential.

Style transfer models leverage deep learning technologies to infuse one image's style onto another's content, preserving the original content while presenting it in a different aesthetic. Prominent software examples employing style transfer models include Spline AI, Change Style AI, and Transpic.

In real estate, software utilizing style transfer models finds application in virtual staging, property visualizations, and the creation of virtual tours.

Benefits of AI in Real Estate

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AI Applications for Real Estate Industry

Integrate virtual assistants and chatbots to ensure smooth communication

Conversational AI stands out in real estate as the ultimate support system, ensuring round-the-clock responsiveness to queries without jeopardizing client relationships. These AI systems promptly address customer inquiries, coordinate appointments with real estate agents, and extend assistance in property searches.

An outstanding illustration of an AI-driven chat and textbot designed for real estate professionals, specifically tailored for property managers, is This tool excels in handling tenant inquiries day and night, streamlining processes for maintenance requests, room bookings, and a host of other tasks. By integrating Chatbots and Virtual Assistants with CRM systems, chatbots and virtual assistants ensure that no lead is lost.

Here are some other Chatbots that use AI:,, RoofAI.

Personalized Property Recommendations

Property marketplaces in the digital realm, such as Realtor, have maximized this functionality. Realtor's Dream Home Builder effortlessly aligns buyers and renters with their ideal properties: users input property descriptions, triggering image recognition technology to display property images on-screen. Consequently, users receive a list of available properties that resonate with their preferences.

Through tailored property suggestions, customers invest less time in property searches, while brokers secure quicker and increased deal closures.

View every detail of your property in 2D and 3D with virtual property tours

Both 2D and 3D tools utilized in generating virtual property tours (such as Matterport and Getfloorplan offer an immersive experience, diminishing the necessity for potential buyers or renters to schedule physical visits to the property.

Typically, 2D generation tools involve users capturing property photos and uploading them onto the virtual tour generation platform for proper arrangement. Conversely, 3D scanners or cameras are commonly employed to craft a three-dimensional representation of the property for the tour. Image4.png

For example, Getfloorplan facilitates brokers in fashioning visually captivating property tours, offering uncomplicated 2D or vibrant 3D plans using the abovementioned steps. This process transforms property photos and videos into compelling property presentations designed to drive sales.

Use Property Flyer Generators to make customer-appealing listings

How many potential buyers might you have missed out on simply due to a cluttered property description or irrelevant details?

Initially, input the property information into the tool, including property size, price, and features. Next, select a template for your prospective property flyer. Subsequently, the generator will apply the chosen style and incorporate property media such as photos and links to virtual tours.

An excellent illustration of an AI-powered listing generator is ListingAI, which utilizes NLP models to assess property data and produce compelling written content.

It would be advantageous if a real estate flyer AI could retrieve property data (photos, descriptions, size, and other relevant information) directly from your CRM or database.

Ensure accurate underwriting evaluations with AVM-powered tools

Automated Valuation Models (AVMs), such as GeoPhy and Quantarium, minimize human error by automating calculations and guaranteeing precise valuations. They achieve this by simultaneously factoring in variables like property location, characteristics, and market trends. Concurrently, tools like Enodo conducts comparative analysis and complete underwriting tasks within minutes.

Predictive analytics keeps you ahead of the market

Most predictive analytics tools within the real estate domain have been crafted to streamline construction and investment procedures. However, tools such as Locate AI possess the potential for use by brokers seeking promising locations or can utilize Tenant ChatGPT, developed by Particle Space, to enhance team collaboration.

Regarding AI advancements for construction and investment, professionals in construction can significantly profit from identifying promising development sites using Deepblocks or monitoring construction advancement through Doxel. Simultaneously, investors can enhance portfolio performance using investment management tools like Kavout or Canoe.

An automated fraud detection model detects anomalies in loan applications in real-time

AI-driven fraud detection models leverage historical data to learn and discern deviations from typical application patterns, flagging such instances as potential fraud. They excel at identifying fraudulent activities that might elude human detection while tracking devices and IP addresses associated with multiple applications.

Among the recent AI-powered fraud detection software available is Ocrolus. This tool utilizes algorithms to automatically categorize financial documents, extract and organize data, detect potential fraudulent behaviors, and comprehend distinctive cash flow dynamics.

Smart Risk Assessment Models for Mortgage Risk Mitigation

Like underwriting software, risk assessment tools frequently integrate with credit bureaus to access updated client data, including a borrower’s credit history. When infused with AI capabilities, these tools can simulate scenarios such as fluctuations in interest rates and their impact on mortgage affordability.

A notable example is CREX Capital—an AI-driven risk assessment platform with Blockchain algorithms. It optimizes property financing through automated risk evaluations, valuation reports, and metric-driven matching.

Improve your home's energy efficiency and management with AI-based home automation tools

AI-based home automation tools encompass a range of features, including virtual assistants like Josh AI, intelligent security systems such as Frigate, and comprehensive home control systems like AI Home Solutions. These tools are crafted to assist users in managing various facets of their homes, from lighting and temperature to HVAC systems, home access, and security setups.

They prove particularly beneficial for landlords and property managers aiming to economize on energy consumption while ensuring tenant comfort. Solutions like BrainboxAI interface with HVAC systems, issuing real-time directives to curtail emissions. These directives consider weather forecasts and historical utility data to optimize energy usage.

Additionally, AI-driven solutions like DEXMA and Gridium serve as distinct energy management systems, gathering data from sources like utility meters for subsequent analysis. Other solutions specialize in overseeing renewable energy sources, such as solar panels. For instance, ONYX Insight is utilized in managing wind turbines.

AI tools for home automation: Homebase AI

Software for managing energy using AI: Clir Renewables, Climate AI

Enhance safety with facial recognition and security tools

Facial recognition technology coupled with security tools such as Frigate thwarts unauthorized property entry by identifying distinctive facial characteristics. These systems promptly issue real-time alerts upon detecting potential threats, with some capable of cross-referencing faces against databases of known criminals. Additionally, they effectively deter tailgating, proving particularly valuable in expansive areas where human monitoring might be less efficient.

Introduce Sustainability with Smart Building Systems

A growing number of businesses have embraced sustainability in the wake of regulations such as the Corporate Sustainability Reporting Directive (CSRD) and Sustainable Finance Disclosure Regulation (SFDR). An impressive 90% of S&P 500 companies now release ESG reports. Consequently, it's no surprise that construction firms are keen on employing ESG platforms like Measurabl. These platforms enable the measurement, management, and disclosure of ESG data with a clear intent to take actionable steps toward sustainability.

They frequently intersect with AI home automation tools and intelligent building systems, often called 'green building' solutions, and aid users in overseeing and regulating energy consumption. These systems are instrumental in curbing energy wastage and commonly integrate with renewable energy sources like solar panels or wind turbines.

Further AI-driven solutions in intelligent buildings and ESG platforms include Greenbuilding Studio and BuildingMinds.

Communicate 24/7 with tenants using chatbots and virtual assistants

In contrast to property managers and landlords, AI-driven chatbots and virtual assistants operate without rest. Indeed, they maintain round-the-clock communication with your tenants, promptly addressing their needs and providing immediate alerts during emergencies. This continuous support significantly enhances tenant satisfaction, thereby bolstering tenant retention rates.

Take, for example, Propic Concierge, an NLP-powered 'conversation assistant.' This solution handles tenant inquiries at all hours, sourced from various channels like Facebook, email, and SMS.

Additional AI software designed for property management includes AUtility and Autohost.

Create consistent interior designs using AI

AI-powered interior design generation tools assist design and property development teams in guaranteeing design uniformity by analyzing colors, patterns, textures, and materials. For instance, tools like Dream Staging AI, among the latest interior design generators, frequently include a digital repository of predefined elements and artistic styles for implementation. In doing so, AI applications within real estate provide instantaneous recommendations concerning design choices and can identify recurring design motifs.

Reduce Construction Errors with AI BIM Software

AI modules integrated into BIM Software aid in scrutinizing 3D building models to ensure optimal resource distribution, thereby mitigating project delays. They ascertain the best construction sequence and identify potential on-site hazards, consequently lowering accident risks, reducing wastage, and enhancing overall efficiency.

An illustration of this capability is Hypar, which enables users to swiftly generate, visualize, and analyze buildings within minutes. This tool seamlessly integrates with Revit, Excel, Dynamo, and Grasshopper.

Software for AI BIM: BricsCAD

Manage your investment portfolio using AI-powered tool

Real estate player can assess their investment risks and potential returns with rental income projections and comparable and demand analysis, ensuring safe investments in a dynamic market. Entera is a great example of an AI investment portfolio management tool.

Artificial Intelligence's Impact on Real Estate

Redfin ChatGPT

In collaboration with OpenAI, the company developed the Redfin ChatGPT plugin. Users of ChatGPT who want to find a new property can use the plugin. Redfin's plugin allows users to communicate with the machine, receive precise property recommendations, and schedule on-demand property tours once it has been installed and activated.

Zillow AI Search

Zillow AI is a user-friendly search feature available to iOS users as of January 2023. A search bar is used to enter queries - no additional filters are required.

Two years ago, Neural Zestimate was introduced as a machine-learning-powered valuation model. Based on a multitude of data points, the model is able to forecast home values accurately.

In order to streamline the real estate experience for users, Zillow is heavily investing in artificial intelligence in real estate. The company will develop a single digital ecosystem designed to simplify the process of finding a home.

Compass Video Studio

Video Studio was introduced by Compass in 2021 as an AI tool for real estate professionals. To use Compass, users must register, select a video template, and feed the listing details.

This tool detects property features, matches them with images, and generates an end-to-end video presentation based on its image recognition mechanism.


AI has already been integrated into real estate by JLL to improve building efficiency, create 3D leasing visualizations, and assess sustainability risks.

The first JLL GPT model for commercial real estate was introduced in August 2023. The model was developed in-house and will be used by over 100 thousand clients worldwide. Using JLL GPT, users can engage in interactive dialogues to gain more actionable insights into real estate space utilization and portfolio optimization.

Zumper PowerLeads AI

Zumper introduced PowerLeads AI in 2021, a system that analyzes renters' activities and predicts their inclination to lease. By identifying renters who are ready to lease immediately, this solution helps real estate agents qualify leads more accurately.

Earlier this year, Zumper ChatGPT plugin was introduced. By matching tenants' property search criteria with Zumper listings, the feature ensures a smooth search experience for renters.

Final words

The integration of artificial intelligence in the real estate sector is no surprise. It catalyzes companies worldwide, enabling significant advancements in data collection, analysis, and revenue enhancement.

However, successful AI implementation necessitates a sturdy groundwork. Before investing substantially in its adoption, ensure that your real estate enterprise possesses robust workflow automation and a dependable set of tools, forming a reliable foundation for integrating artificial intelligence technologies.

Vetted experts, custom approach, dedication to meet deadlines

As your reliable partner, our team will use the right technology for your case, and turn your concept into a sustainable product.

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