Artificial Intelligence in Real Estate

Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) and Natural Language Generation (NLG) are currently being used in the real estate industry. We reviewed hundreds of research papers to save you time in selecting your next AI project.

1) Finding the Market Value of a Building

Predict demand in the market depending upon the location and features of a listing.

Predict house prices based on its location, age of the structure, living spaces, number of rooms including bedrooms and bathrooms, energy efficiency, and the quality of life in the area. It also considers the type of property, commute time, and mode of transport involved.

2) Automatic Document Scanning

AI helps in the scanning of documents by identifying red flags and key terms. It uses natural language processing (NLP) for scanning and does it in quick time to obliterate the need for manual due diligence.

3) Predicting Long Term Value (LTV)

Predict long term value (LTV) of new listings.

4) Predicting Customer Lifetime Value (CLV)

AI helps in the prediction of long term value of listings.

5) Image Recognition

AI can classify images to help in search of similar properties for comparison.

6) Classify User Needs

AI can help in identifying user needs by using NLP and analyzing user behavior and content generated by him.

AI can also help in finding unique aspects of a property using NLP.

7) Profile Matching

Machine Learning, an advanced form of AI, can carry out analysis of past deals and interactions. This helps property owners, real estate agents, and tenants in understanding parameters for matching offers.

8) Automated Underwriting Process

Machine Learning can be used to analyze historical income data. This helps in automation of underwriting process of commercial mortgage.

9) Generating Real Estate Listing Bios

Machine Learning can use Natural Language Generation (NLG) to produce high-quality listing bios for realtors to be used in their websites and profiles of LinkedIn.

10) Commercial Property Segmentation

AI and Machine Learning can be used to combine commercial properties in different categories.

11) Mortgage Backed Security Portfolio Analysis

In a time when there is a boom in refinance and defaults, Machine Learning can be very helpful in predicting prepayments.

12) Predicting Value of Property

Machine Learning has proved to be a great help in finding the approximate value of residential properties. It can do so through analysis of large amount of data that is collected from various sources like census roles, police records, social media, and places like schools and grocery stores.

13) Classification of Seller Score

Machine Learning can tell you how likely a property owner is to sell to you. This is done through analysis of data that includes demographics, income levels, events in his life, his purchasing behavior, and so on.

14) Targeting Real Estate Markets

Cutting edge Machine Learning technology called Extremely Randomized (ER) Trees can be applied to identify and rank markets according to their performance. ER Trees makes use of traditional data and alternative data sets.

15) Predicting Time to Close

Machine Learning technologies can also predict the expected time of closing a home in a market taking into account factors like market cycles and season.

16) Predicting Time to Call

Machine Learning can tell you what the best time to call or send an email is.

17) Predicting Where to Focus Marketing

Machine Learning can help in identifying the right media to attain the goals of marketing. It can help in saving your time, effort and money on marketing.

18) Predicting Customer Language

You can learn what language and tone to use with a customer with the help of Machine Learning.

19) Effective Lead Management

Machine Learning can analyze historical sales record to predict the properties that are most likely to sell within a time frame.

20) Automated Property Valuations

Machine Learning is helping in automated property valuations.

21) Chatbot Assistants

Chatbots are helping in sales of properties.

Chatbots can be used to get an office or property on lease.

Chatbots can answer questions regarding the availability of space and to register for open houses. They can also schedule appointments.

Customers can ask questions to chatbots about properties suitable for them. They can create customer profiles to easily develop relationships.

22) Predict Zoning Developments

Machine Learning can predict what kind of zoning developments is likely to take place in a community.

23) Buy and Sell Properties

You can identify potential buyers using Machine Learning that analyzes clicks on your ad and the recent purchasing decisions of these customers.

24) Maximize City Space

Analysis of Big Data through Machine Learning can give an idea about potential developments in a city.

25) Enhance Building Automation

Analysis of data gathered from the internet of things devices, it is possible to improve the automation of buildings.

26) Mortgage Fraud Detection and Prevention

Mortgage fraud has gained prominence. Mortgage fraud is any scheme designed to obtain a mortgage under false pretenses. It can be a simple act of falsifying information on a loan application or more sophisticated schemes involving one or several parties with the intent of defrauding a financial institution and other innocent parties of money through a mortgage loan. Luckily, machine learning models are ideally suited to recognize and detect early and late signs of mortgage fraud.

27) Title Defect Detection (or Cloud Detection)

It’s possible to use machine learning to title defects, also known as clouds. A title defect refers to any potential threat to a current owner’s full right or claim to sell a property. AI system can be used to detect errors in the public records, mechanic’s liens, bankruptcies, liens for child support, liens for past-due spousal support, unknown liens, delinquent taxes, illegal deeds, undiscovered encumbrances, unknown easements, boundary/survey disputes, missing heirs, forgeries, undiscovered wills, or false impersonation of previous owners.

28) Title Fraud Detection

Incidents of real estate title fraud are very common and
homeowners and lenders are irresistible targets for fraud artists. Real estate title fraud against a homeowner occurs when someone fraudulently uses a homeowner’s identity to assume the title to their property and then sells the home or takes out a new mortgage. Detecting title fraud is now possible thanks to machine learning models which solve anomaly detection, face verification and face recognition.

29) Title Insurance Policy Recommendations

Recommendation system can be built to offer suggestions for insurance policies and packages. Residential title insurance policies can insure houses, condominiums, cottages, rental units, vacant land, cooperatives, leased properties, and rural properties. Commercial title insurance policies, on the other hand, include office buildings, industrial buildings, shopping centers, apartment buildings, rental units, warehouses, vacant commercial land, and leased commercial properties.

AI Milestones

Broker vs. Bot Challenge conducted by Inman in 2016 showed what AI can do in the field of real estate. A bot defeated wisdom of three realtors combined in identifying the preferences of a buyer. Bot won the challenge and paved the way for AI in real estate.

There are many AI and ML ideas to explore when it comes to the real estate industry. Companies and individuals can use these AI ideas to fund research and development projects or develop full solutions.

1) Predict Market Bubbles

Machine Learning can be used to predict changes in a housing market based upon inventory, interest rate changes, annual income changes, and monthly rents.

2) Report Generation

Machine Learning and natural language generation can help in doing research about a housing market to prepare a consolidated report.

3) Risk Monitoring

Deep learning can identify risky market trends.

4) Answer Questions Using Chatbot Assistants

Chatbots can be used to answer queries of customers about leasing terms.

Realtors can use chatbots to take visitors to the page containing information they are seeking.

5) Investor Analytics

Analysis of risk and financial projections through Machine Learning can help investors in setting goals for income and growth.

6) Deal Matching

Investors can use Machine Learning to identify properties that match their criterions.

Investors can also use Machine Learning to stay away from properties that do not fall within their investment parameters.

7) Construction Automation

Builders can use AI to automate their purchases of materials.

8) Property Management

Machine Learning can predict when it is time for maintenance or replacement of systems through monitoring of data.

By analyzing features that impact rents and expenses, Machine Learning can help in building automation and expansion.

9) Enrich CRM Data

AI can integrate with CRM to analyze leads that are most likely to become customers.

Broker vs. Bot Challenge conducted by Inman in 2016 showed what AI can do in the field of real estate. A bot defeated wisdom of three realtors combined in identifying the preferences of a buyer. Bot won the challenge and paved the way for AI in real estate.