Machine learning is itself a serious discipline. It is actively used around us, and on a much more serious scale than you can imagine. Its popularity has increased many times over recent years, and every new real estate company is trying to use these technologies to turn the traditional market around.
The flow of information available to owners and operators of real estate is growing every day. The problem is that only a small fraction of this data is used. It is no exaggeration to say that this industry has almost endless possibilities for unrelated data points. Many of them are often ignored when they can be used to support bottom-line performance and tenant satisfaction. This black hole of ignored data can be called “data abyss”.
As a rule, this gap accounts for 97% of relevant data, which means that operators and homeowners use only 3% of the information available to them.
This neglected data can be found in many isolated places, including internal platforms such as Microsoft Dynamics, MRI, Yardi, Salesforce, and VTS. Industry and market studies are also important sources, providing information on local property, such as weather trends, closed leases, vacancies, and data on tenant business sectors. Other data channels include information on tenant experience (from applications such as Equiem or HqO) and data from HVAC or others. At best, for future commercial property, landlords would need to find a way to connect these sources and actively use the information to generate smart data that could allow them to cut costs and increase revenues. However, solving the data abyss problem is too much for any individual.
One of the good features of artificial intelligence is its ability to detect what the human eye misses. It uses machine learning algorithms to capture anomalies, nuances, and complex patterns that are likely to be left out of a person’s analysis. Yes, a person could well sift through an organized spreadsheet and identify a template, but the AI can explore the vast amounts of information from a dozen different sources and use this template to predict how it can affect a portfolio or building in a broader sense.
Using it as a tool to analyze this information, the possibilities of machine learning are almost endless. For example, for apartment buildings, a machine learning mechanism can view both property information and market data, and analyze various characteristics that affect local rental rates. It also helps to lease professionals better define their strategy for renting free apartments. By the unique characteristics inside the building and the demand in the market, it can be assumed that apartment prices at a lower price should increase the total income of the building.
In some cases, based on local data, it can determine that it is advisable not to put real estate on the market now because it will bring short-term income, but it will make more financial sense after the kitchen is repaired and only then you can try to rent the renovated apartment.
In the case of tenants, an artificial intelligence tool may notice that in certain weather conditions with a certain profile they may feel inconvenience. In order not to wait for tenant complaints, the platform will immediately report about a probable problem that may soon appear to the appropriate property manager so that he can take appropriate action. In the same way, it will be able to identify the problems with the elevator, which usually arise when the cold weather persists for several days, and warn the engineer about the need for preventive measures.
It also allows individuals to work with the amount of data that exists in the abyss. The abundance of data from all relevant channels is so great that for analysis without the use of artificial intelligence, it can take a very long time. And after it, most of the information will no longer be timely. Instead, housing managers ignore most of the relevant data because of the time that will be needed to analyze it.
Using machine learning, which analyzes real-time data from existing systems, homeowners will be able to receive information in a timely manner, allowing construction professionals to quickly move forward using their capabilities.
Artificial Intelligence overcomes the abyss by supporting real estate professionals, warning them of problems and opportunities that would otherwise be missed.
Digitalization of various applied industries continues, so we also decided to find interesting cases for you, such as big data, machine learning and the Internet of things used in real estate. You will learn how they are useful to builders and realtors, and how the introduction of these technologies will help consumers.
Big Data for Forecasting Demand for Real Estate
By collecting data on user interactions with websites of real estate agencies and construction companies, opinion polls, statistics on the urban population, economic surveys, development plans for urban areas and the transport system, one can predict the future needs of clients in various types of real estate.
For example, to determine in which areas residential property will be in demand in 10–20 years. Experts have hypothesized that in areas with a maximum share of children, future buyers of apartments are concentrated. These are already established families that will need to expand their living space, as well as new cells of society. Thus, construction companies can formulate proposals in advance, which are 100% in demand.
Machine Learning for personalized apartment advertising
After analyzing the available data about customers and supplementing them with information from open sources, such as social networks, you can create a very detailed portrait of each consumer: his interests, financial capabilities, expectations and needs. After this, it is advisable to launch personal marketing campaigns for real estate advertising: individual parking for those who boasted a new car on Instagram, large apartments for those who are expecting a baby, closed courtyards with playgrounds for families with children, and much more.
Big data technologies can reveal even among newlyweds those who are highly likely to get divorced over the next two years by analyzing their behavior on social networks. Each of the pairs of divorced people will need new housing, as well as people who live together for a long time and possibly think about children and expand their living space. Having received, using machine learning, the behavior patterns of people who are thinking about buying an apartment, it is advisable to show the marketing offer only to those who are as close to buying as possible. This will bring high results and reduce advertising budget costs.
Similarly, you can create advertising campaigns for commercial real estate. For example, by collecting information about companies from open sources and social networks (number of employees, area, job information from job search sites, information on opening branches and regional expansion, etc.), you can make a list of potential consumers. A targeted marketing campaign aimed at these growing companies can bring greater sales conversions and save an advertising budget due to the narrow specialization of the target audience.
In addition to the content of the advertising proposal, with the help of Big Data you can increase the effectiveness of marketing campaigns thanks to the implicit laws of business metrics from external conditions. For example, if machine learning models have shown that the intensity of calls to real estate agencies and construction companies depends on the season and weather, it is logical to increase the percentage of banner ad impressions during this period. Also, if there is a correlation between the number of search queries and the time of day, it is advisable to increase the conversion by buying morning or, conversely, evening displays.
Internet of Things for Smart Building Management
“Smart” houses, where you can set a comfortable temperature through a mobile application or prevent a robbery, are gradually becoming the standard in the construction of new buildings. Sensors and sensors monitor the flow of electricity, water and gas to detect leaks in a timely manner or optimize resource consumption.
For example, Silverstein, a major development and management company in New York, having implemented Internet of Things technologies at its facilities, has reduced energy costs by analyzing utilities and connecting efficient energy storage during peak hours. It was also possible to significantly reduce energy consumption due to the system of LED lamps, which provides advanced lighting control, corresponding to the time of day or occasion. In addition to reducing financial costs for the operation of the building, the optimization of energy consumption also has a positive effect on the environment.
Another interesting example from the USA on the use of Big Data in the real estate management sector is related to mobile applications for residents of luxury residential complexes in the city of Miami. By analyzing the movements of residents, their behavior statistics and data on the current date from the calendar (weekdays, weekends, working days or holidays), Machine Learning algorithms calculate the optimal time for calling the elevator, automatically order coffee and a car at the right time for each guest.
Big Data, Machine Learning and Internet Of Things allow you to combine local “smart homes” in a single infrastructure for complex and remote management. In particular, if in one of the houses a heat supply pipe suddenly burst, the backup water supply system will turn on automatically so that residents do not remain without water or heat. A large power capacity will also be reserved so that the power grid can withstand the load increased by the use of domestic electric heaters. At the same time, all services involved in the process of eliminating the accident and its consequences will be notified. To organize safe repairs, the principle of the road network arrangement in this quarter will change, for example, the boards installed along the road will show a detour circuit.
It should be noted that in 2020, the ability of Artificial Intelligence to expand operations with property ceased to be a futuristic idea. Now this is already a proven reality. As a result, the predictive ability can be used to increase tenant comfort and rental speed, which will increase the level of NOI.
It’s true that at first the real estate industry was in no hurry to introduce new technologies, but over the past few years it has gained momentum. But while obvious progress has been made, only a few companies have actually begun to use machine learning capabilities to increase housing operations.
As the cycle progresses, more competitors appear in this area, and soon a much higher level of complexity can be expected from managers and owners. But the data in the buildings that are in use right now is small, although this is changing rapidly. One day we will be able to enter the real era of big data in real estate and realize all our desires.