Artificial Intelligence, Machine Learning, and Deep Learning are revolutionizing the real estate industry. Here’s what you need to know.
Real Estate Industry
What is real estate?
Real estate includes both the parcel of land and the structure built upon it. It also includes all-natural resources found on the property such as water, minerals, and crops. Real estate refers to immovable property, particularly housing units and buildings. With such terminology, it becomes clear that the profession of real estate involves buy, selling, and renting of land parcels, housing units, and commercial buildings.
Who will benefit from reading this?
This blog is intended for anyone involved with the profession of real estate, including, but not limited to: realtors, property managers, assessors, appraisers, real estate agents, and brokers. We want to make this blog an extremely useful and valuable resource for everyone.
Types of Real Estate
It is important to differentiate between commercial and residential real estate, in order to better understand this complex industry.
- Commercial Real Estate
Real estate that is used to generate profits or income is termed as commercial real estate. It is commonly referred to as investment or income property. Any property that contains more than five housing units and generates rental income for the owner is called Commercial Real Estate (CRE). However, the term CRE is mostly reserved for buildings containing offices, retail showrooms, or factories. Commercial real estate investing is a business that attracts people who have knowledge, experience, or the resources to make investments.
2. Residential Real Estate
Real estate that consists of single family homes and buildings with up to four residential units is considered Residential Real Estate (RRE). RRE also includes all cooperative units as well as condominiums. One can become a real estate investor by simply buying homes and rental properties. Those who fix, and flip homes are also called real estate investors. Flipping is the act of buying a home at lower than its market value in as is condition, repairing it, and then selling it at a higher price for profit.
Artificial Intelligence
Artificial intelligence and machine learning is revolutionizing the mining industry. Machine Learning is a growing and diverse field of Artificial Intelligence which studies algorithms that are capable of automatically learning from data and making predictions based on data. Machine learning is one of the most exciting technological areas of study today. Each week there are new advancements, new technologies, new applications, and new opportunities. It’s inspiring, but also overwhelming. That’s why we created this guide to help you keep pace with all these exciting developments. Whether you’re currently employed in the real estate industry, or just pursuing an interest in the subject, or working with us at Produvia, there will always be something here to inspire you!
Practical AI 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 type of property, commute time, and mode of transport involved.
2) Automatic Document Scanning
- AI helps in 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 of 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 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 about 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 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 internet of things devices, it is possible to improve automation of buildings.
Future of 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.
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.
About Produvia
Produvia is an Artificial Intelligence agency based in Vancouver, Canada. We develop custom AI and Machine Learning software. Work with us to accelerate the adoption of AI at your company.
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Artificial Intelligence (AI) in Real Estate was originally published in Produvia on Medium, where people are continuing the conversation by highlighting and responding to this story.