artificial intelligence real estate

How AI is Transforming the Real Estate Industry?

Estimated read time: 7 minutes

Artificial Intelligence (AI) in real estate is proving its mettle. AI is transforming the real estate industry in the following ways:

1. Better lead generation for real estate companies

Real estate agents often find it hard to generate high-quality leads for a property. They might have multiple discussions with prospective buyers. They spend many hours in this process. However, they can’t get the conversion.

This problem becomes worse for busy realtors. Real estate professionals use their local expertise and other skills to generate leads, however, the conversion rate might remain low.

AI’s ability to gather high-quality insights from vast data sets can help them. Plenty of data exists to gain insights into customer behavior. However, it’s massive and often unstructured. AI capabilities like machine learning (ML) and natural language processing (NLP) can quickly gather actionable intelligence from vast sets of unstructured data.

Zillow, a CRM-integrated AI platform is an example. This online real estate marketplace uses intelligence AI algorithms to generate quality leads.

2. Real estate professionals and companies can market their content better

AI-based real estate technology solutions help real estate companies with content marketing. One approach involves AI chatbots. Busy realtors might find it hard to respond to customer queries quickly, however, AI chatbots can respond immediately.

These chatbots can provide content to potential buyers from a content repository. They can also ask preliminary questions to prospective buyers about preferred locations, budgets, etc.

The second approach involves creating real estate ads for different social media platforms. AI tools can then display relevant ads to prospective buyers. PropertySimple, a real estate social media marketing platform is an example.


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3. Real estate companies can find the right property values

Investment-focused real estate companies would like to buy properties at the optimal price. They would want to sell properties at a healthy profit. They need to analyze the current and future home prices accurately for this, which is a tough job.

Property prices don’t just depend on a few things! Many factors influence current and future property prices, e.g.:

  • Likelihood of natural disasters in the concerned region;
  • Park proximity;
  • School proximity;
  • Crime rate;
  • Infrastructural facilities;
  • Community;
  • How much light the property gets;
  • Home buying trends in the locality.

Literally determining prices of properties based on that many data points can be hard even for real estate experts.

AI-powered real estate platforms can help to evaluate current and future property prices. HouseCanary is an example. It uses AI to evaluate the current and future prices of properties. is an AI start-up that analyzes how much light a property receives. The platform identifies the sunniest hours, and it identifies the darkest buildings in localities.

4. Home searches become easier for potential buyers

Prospective buyers visit multiple real estate apps and websites and view the property listings. They use multiple search criteria like location, square footage, price range, etc. Despite many online searches, they often don’t get what they want.

Consumers prioritize specialization and choices these days. Search engines reflect this user behavior in many industries, and the real estate sector isn’t an exception.

Real estate companies can’t continue to use the old ways and meet the specialized customer search preferences anymore. They need to learn more about their prospective buyers. Real estate companies need to make personalized recommendations.

Just as in any other industry, AI can help real estate professionals to make personalized recommendations. Trulia, an online property marketplace does exactly that.

This AI-powered platform simplifies customer search and offers personalized property recommendations. Its AI algorithms understand visitors’ preferences by analyzing previous searches and other relevant information.

5. Property owners can better manage their properties

Property owners and rental companies need to manage their rental properties. They need to take care of many aspects, e.g.:

  • Collecting prospect information from different websites and apps;
  • Responding to queries from prospective clients;
  • Processing rental applications;
  • Managing rental transactions;
  • Creating lease documents;
  • Preparing disclosure statements.

This takes a lot of time and manual effort. Smart AI assistants can take care of several manual tasks. These can include AI chatbots that respond to queries. AI-powered tools can create documents quickly, and they can detect errors in documents.

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LetHub is an example. This AI platform for rental property management claims to use AI and a human touch for rental property management.

LetHub includes a chatbot that answers questions from potential clients. It schedules property tours and pre-qualifies prospects. LetHub reduces data entry with the help of a CRM system. It improves pricing and customer service decisions by learning from feedback.

6. Managing space efficiently

Real estate prices are very high in many cities all around the world. Companies spend big money to acquire office spaces in these cities. Inefficient management of office space can cause significant loss, however, space management is tricky.

Companies frequently change the strengths of their departments. They also buy new equipment and get rid of old ones. Their office space planning and management activities can’t keep pace with these changes, therefore, office spaces aren’t utilized efficiently.

Together, AI and IoT can help. IoT sensors collect data about office space usage. AI solutions can collect this data, furthermore, they can collect data from organizational Wi-Fi systems too. AI algorithms can quickly pinpoint inefficient usage of office space.

The IBM TRIRIGA Application Suite helps organizations manage office space efficiently. This Integrated Workplace Management Solution (IWMS) uses AI, analytics, and IoT for this.

7. Hiring better real estate agents with the help of artificial intelligence

Real estate companies need competent real estate professionals to tap into new opportunities. Entering a new and unfamiliar market can be hard. Experienced professionals can make a positive difference. Real estate companies also need to replace agents in the case of turnovers.

The hiring process certainly needs a significant human touch. However, it includes plenty of manual tasks too, e.g., screening resumes. AI-powered hiring solutions can handle these tasks. You can also remove biases from the hiring process by using AI tools. These help you to hire smarter professionals.

8. Improving the buying and selling process with the help of AI tools

The buying and selling processes in the real estate industry are complex. These processes involve many stakeholders, e.g.:

  • Buyers;
  • Sellers;
  • Brokers;
  • Agents;
  • Mortgage lenders;
  • Other relevant departments in real estate companies.

The real estate transaction process involves many steps, e.g.:

  • Editing forms;
  • Signing forms electronically;
  • Producing and managing the relevant documents;
  • Compliance management.

Real estate companies need appropriate technology solutions to reduce the complexity. AI-based solutions can help. These solutions can help with proper resource planning. AI matchmaking tools can help real estate companies find prospective buyers. AI tools can reduce data entry too.

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Dotloop, a real estate transaction management solution is an example. It helps busy realtors manage the entire gamut of real estate transactions. Dotloop runs AI algorithms for this. It also integrates with Cloze CRM, which reduces data entry with the help of AI.

9. Helping real estate investors to make better investment decisions

Real estate investors need decision-making support to make wise buying decisions. Actionable insights are important for this. Investors need to know about high-reward opportunities. They need to know about distressed conditions in the real estate market.

Actionable insights will come from various real estate data. This could include market trends, commercial real estate prices in specific regions, real estate development costs, etc. Real estate investors can get a competitive advantage with valuable insights.

Skyline AI is an AI-powered real estate platform. It utilizes predictive analytics to identify rewarding investment opportunities from the real estate market data. It provides an extensive analysis of the commercial real estate market, and its speed helps investors.

10. Helping mortgage lenders by improving the lending process

Mortgage lenders are typically banks. They perform an extensive screening process before they provide loans to prospective buyers. As a part of this process, they collect vast sets of documents. They also collect relevant information from various sources.

Subsequently, they need to gather actionable intelligence from this information. This process is very time-consuming if done manually. Banks are increasingly using AI capabilities like ML and NLP for this. The smart algorithms offered by AI platforms extract insights from vast data sets quickly, which expedites the screening process for banks.

Planning to develop artificial intelligence and machine learning applications for the real estate market? Contact DevTeam.Space to hire smart developers.


1. Which are the top real estate apps that use AI?

TRIRIGA from IBM, Doxel,, Houzen, Hyro, Rex, Zillow, Compass, Redfin, HouseCanary, Trulia, Apartment Ocean, GeoPhy, Veros, Nobbas, and Skyline AI are the top real estate apps that use the AI technology.

2. Which are the top start-ups that utilize AI in real estate?

Top start-ups that utilize AI in the real estate industry are as follows: Cherre, DeepBlocks, CityBldr, Jointer, Blok, Proportunity, Ojo Labs, Reonomy, Enodo, CoreLogic, Skyline AI, Veros, Redfin,, and Doxel.

3. Which programming language is best for developing machine learning apps for the real estate business?

We recommend you use Python to develop a machine learning app for the real estate market. Python offers excellent libraries for AI, ML, and data science projects. It’s also one of the most popular languages with a bright future.


Alexey Semeney

Founder of DevTeam.Space

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