Artificial Intelligence in the Real Estate Sector
Artificial intelligence (AI) tools are software programmed to learn and self-improve, with the aim of consolidating and speeding up even the most complex processes. In the real estate sector, this translates into the opportunity for sellers, agents, property managers, and investors to structure their work in a much more efficient way, and to save on transaction costs. AI has the potential to revolutionize the real estate industry, making it much more innovative.
Through the use of machine learning, applied to Big Data and IoT, many aspects of real estate can be modernized: from data collection and management aimed at buying and selling, to the maintenance of the property itself. Here are some examples of how the new AI technologies can be linked to the real estate market.
Analysis of data for marketing purposes
The huge amount of consumer data collected through AI-enabled applications can be used to create lead generation and make content marketing campaigns more effective. Real-time analysis of data makes the consumer’s preferences evident and enables the sending of targeted proposals, taking into account not only the parameters expressly selected by the potential client but also those resulting from their own consumer experience.
Automatic document compilation
AI can be used to automate the compilation of reports useful for customer relationship management (CRM). Through specific applications such as NLP (Natural Language Processing), documents can be scanned to identify any inaccuracies and inconsistencies, reducing the risk of human error in manual data entry.
Assessment of the propensity to buy/sell
Machine learning can be used to analyze historical data on a potential customer’s income, allowing the actual economic capacity of customers to be evaluated in order to discard those who do not have real purchasing opportunities. Through income data screening, it is also possible to evaluate creditworthiness and automate the process of subscribing to commercial mortgages. Likewise, the machine learning algorithm can assess the probability of the sale taking place by analyzing the seller’s income, life events that may affect the sale, their behavior, and so on.
Whether it is a commercial space or a residential property, the estimate of the property’s value is influenced by many factors, referring to data in public records, the presence of adequate educational services, transportation, crime rates, green areas, pollution, and so on. These are thousands of data that are certainly not within the reach of “human” analysis but that can be fed into an AI algorithm to make not only a current estimate of a given market but also a predictive analysis of the same.
The estimate of the property’s value can be made not only for the benefit of sellers/buyers but also for credit institutions. Before granting a mortgage, the bank has every interest in knowing the value that the property will have on the market (especially in auctions) in the long term, to understand the actual value of the mortgage if it were to be put back on the market to enforce the credit guarantee.
Prediction of property maintenance
Companies like Agedi, which deal with real estate, can easily keep track of maintenance activities carried out on properties for sale or rent in their portfolio. By monitoring such data, with the use of AI software, it is possible to identify the most common maintenance problems and fluctuations in intervention prices by area/season.
Detection and prediction of mortgage fraud
Mortgage fraud occurs when false information is provided to the lending institution to obtain a mortgage for which one does not actually have the requirements. Machine learning models, with their ability to screen large data flows, can help to detect and anticipate all situations where documents are falsified and artificial pretexts are presented for obtaining financing.
Use of chatbots in sales support
Some real estate agencies are already using chatbots as virtual assistants to manage sales/rental activities. Chatbots can be used as actual virtual assistants to schedule appointments for property visits, answer questions about property features, ask questions to profile the customer, and more.
Conclusions
The examples mentioned above are just a glimpse of the vast potential of these technologies, which should always be considered as an aid and not a substitute for the human component.