How AI is Reshaping Real Estate

05 mars 2024

From Chatbots to lead generation, Artificial intelligence(AI) has found numerous use cases in real estate. It has shifted how realtors and customers consume data, providing tools that enhance this sector

A global industry analysis of AI in real estate shows exponential growth. Its impact on the sector is expected to hit a valuation of $1335.89 billion by 2029, growing at a CAGR of 35% between 2022 to 2029[1]. 

In a survey by Altusgroup, 73% of leading commercial real estate executives interviewed see AI as beneficial bearing practical applications[2]. This article examines how AI is reshaping real estate, its benefits and challenges.

Five Ways AI Impacts Real Estate Activities Sector

Combining AI technologies like IoT, cloud computing, and big data has helped spur the adoption of AI in real estate. Stakeholders are harnessing the output of these technologies to optimize operations, automate routine tasks and much more.

Property management

AI can help streamline and automate workflows, reducing repetitive tasks that property managers handle. Using smart property management solutions, it’s possible to automate routine tasks like tenant screening, renewing leases and scheduling maintenance.

Tenant screening: Machine learning(ML) algorithms like linear regression can help with tenant screening. Models can analyze a prospective tenant’s information, like credit history and rental references, to determine suitability. 

Tenant churn: Every property manager seeks the retention of good tenants. Property management software can integrate churn models that forecast tenancy levels[3]. This helps the manager target and incentivize clients unlikely to renew their lease. It provides a better understanding of tenancy levels across the year, helping the estate plan better.

Lead generation: With 53% of marketers spending half of their budgets on lead generation, it’s imperative that the investment yields dividends[4]

The ability of ML models to deal with classification problems can help segment potential tenants from a leads list. They recognize patterns from data like purchase patterns, relevant social media activities, and browsing history to help identify prospective tenants. However, the flagged clientele type will depend on the mapped-out features and the solution’s capability.

Predictive analytics

Through predictive modeling, commercial real estate professionals can accurately determine market pricing, property developments and other trends. 

Using historical data such as rental growth, sales, and tenancy levels, ML models can identify patterns and correlations that can be extrapolated to give forecasts. With such information, it’s possible to tailor marketing strategies and develop promotional material to attract clients.

Linear regression, decision trees, random forest and neural networks are some algorithms that can be used for predictive modeling[5]. Companies like Buildingiq use predictive modeling to control operational costs and keep tenants' costs low with solutions like predictive energy optimization[6].

When data from IoT sensors are combined with predictive models, property managers can benefit from predictive maintenance. For instance, sensor readings from gas valves, lighting equipment, HVAC, and water supply can be analyzed by time series models to predict the next potential failure[7].

Property search and recommendation

Real estate companies can use recommender systems to help prospective property buyers get suitable matches. Such systems infer users’ preferences from data like browsing and search activities to provide intelligent, personalized suggestions.

According to Zillows’ CTO, David Beitel, the company is looking to use AI to make it easier for customers to find suitable homes[8]. Their AI-powered feature lets users search for homes using natural language powered by NLP algorithms[9]. Clients can enter custom queries to the platform which retrieves possible matches.


Applying AI-based techniques to marketing workflows can help streamline sales and marketing efforts. Some of the prime areas where AI-driven tools are applied in real estate marketing include 

Virtual assistants/chatbots: These use NLP to interact with buyers and tenants via chatbots or by integrating them into messaging applications like email. They can respond instantly to queries using their knowledge base, handle maintenance requests, and offer information about the property. 

Virtual assistants help property agents respond to common queries in under five minutes, increasing the chances of landing a potential client[10]. For instance, Hyro is a company offering AI assistants that help resolve 85% of repetitive tasks[11].

The 24/7 availability of these tools enhances customer satisfaction, helping harvest leads and relieving realtors of mundane tasks.

Home Visualization platform: Realtors already use AI-powered tools to help buyers imagine their perfect homes through virtually redesigning rooms. These tools use image processing algorithms to manipulate images[12].

For example, Redfin, a real estate brokerage firm, offers customers an AI tool to virtually alter a room’s colors, floor, walls and much more[13]. 

Virtual tours: Real estate agents can use 3D models to create virtual replicas of properties on sale or in development. AI is used to improve the model's accuracy, speed and efficiency. Additionally, it is also possible to use generative modeling to create 3D content. 

Potential buyers can tour the property using virtual augmented reality. This helps with realistic views and interactions, which gives customers a better understanding. Property listings with virtual tours get 87% more views, which helps with marketing[14]. 

Virtual tours help reduce drive time, boost the confidence of prospective buyers and attract a better clientele. Additionally, insights generated from these engagements can help developers and real estate professionals tailor better products for the market.

Property valuations

Property valuation models use attributes like market factors, location and property characteristics to give the value of the property automatically[15]

Relying on humans for property valuations sometimes results in infrequent property observations, leading to controversy. Automated property valuations(AVMs) use ML models to analyze and process data for better valuations. 

An example is Zillow’s Zestimate, a valuation tool that uses data curated from users, sales history, location, and more[16]. It offers an unbiased assessment of the tentative value of a property.

Benefits and challenges of using AI in property management

There are numerous benefits and challenges that realtors experience by using AI. See some below:


Automating mundane tasks: Real estate management involves lots of activities, some of which are repetitive and time-consuming. AI-based solutions can easily automate such tasks. For example, AI-based solutions can be programmed to send reminders to tenants for rent collection. This translates to direct savings in time and human resources, leaving property managers to concentrate on more productive engagements.

Enhancing personalization and customization: Property marketers can better target potential clients using AI. Tailoring marketing content and the use of recommender systems using AI is possible. Additionally, AI-powered solutions have made it easier to harness leads.

Improve customer experience: Virtual reality(VR) tours and room redesigning are some AI-enabled solutions that improve customer experience. Consequently, these AI-driven tools also lead to more conversions.

Streamlined property management: AI-powered solutions can help managers screen tenants, predict tenancy levels, and generate leads. Chatbots can help respond to queries quickly, enhancing customer satisfaction. Additionally, predictive models can assist in preventive maintenance of the property, ensuring optimal operational efficiency.

Improve efficiency: Using AI for marketing has helped realtors avoid the “spray and pray” technique, which offers minimal value in return. Additionally, solutions like AVM improve efficiency in property valuation by correctly capturing all factors and cutting the time to perform valuations to minutes.


Some of the challenges facing the use of AI in real estate. 

Ethical considerations: The use of AI is prone to ethical issues, primarily because of data privacy and bias. For instance, while screening tenants, an AI system can profile specific customer segments with certain biases leading to adverse outcomes. 

High initial cost: The initial cost of setting up or integrating an AI solution is expensive. However, costs vary depending on the project's complexity, the number of people involved and the type of software. Estimates put the price of a custom AI solution at $6000-$300,000[17]. Other associated costs include computing resources, implementation fees and maintenance costs. 

Privacy and security concerns: There’s concern about how personal data is collected, stored and processed by AI firms. AI tools must ensure proper data handling in compliance with data privacy regulations like the EU’s GDPR. There must also be adequate security to prevent system breaches and leakages.


Research suggests that the global real estate market is warming up for disruption by AI. Executives in the industry also admit to its benefits and the technology's practical use cases.

AI techniques like machine learning, deep learning, and natural language processing have a tangible impact on real estate. As digital transformation continues to shape real estate(proptech), AI offers different ways to streamline, innovate and enhance real estate processes and workflows.

AI uses cases such as property valuation and virtual tours illustrates how AI bridges the gap between realtors and clients. However, challenges like high initial cost and data privacy continue to hinder the uptake of AI in real estate.


Written by Baptiste Aelbrecht & Jacques Mojsilovic


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