CRE Meets Artificial Intelligence –

It has been at least a few years since apartment owners have discovered the benefits of incorporating chatbots into their operations. Automated customer service technology usually found on the Q&A page of an apartment’s website, these bots provide guidance to potential renters who may have further questions.
When the pandemic upended life in March and social distancing became the norm, apartment owners that didn’t have these apps rushed to implement them. While commonplace before the pandemic, they are now considered essential technology.
A quick look under the hood, though, and you will find that while these chatbots are ubiquitous, they are not all built the same. Some are much smarter than others. These bots, namely, are being run with artificial intelligence and they are delivering a far more robust experience for both the tenant and the apartment owner.
The difference can be profound, says Alec Page, vice president of RET Ventures. AI-fueled bots can handle a wide range of tasks from scheduling a tour to having an advanced conversation about the amenities at a certain building. “These are not decision tree model bots,” he says, which typically have a limited number of responses available to it.
AI-based bots are still in the minority but Page foresees a day when they are almost as common as their older counterparts.
“We are seeing a lot of adoption around building natural language processing tools for bots, for example. These bots are able to have advanced conversations much like a leasing agent could.” The bots aren’t meant to replace the leasing agent, he adds. Rather, the typical scenario is that the leasing agent comes into the office and picks up where the bot left off.
Chatbots are just one area in which AI has begun to meet up with commercial real estate to take processes and procedures beyond the confines of mere automation. To truly understand the implications of this, though, a brief explanation of AI is necessary. At its most basic, AI is technology that is capable of performing tasks that usually require human intelligence. It goes beyond, for example, predictive analytics or decision-tree modeling to process information and think like a human.

For anyone following commercial real estate’s adoption of high tech in the last decade, it may be hard to grasp that AI is being used in a range of activities today. CRE, for years, was seen as a laggard in advanced technology adoption, an image that was finally shed years ago. AI, though, is the height of advanced technology and computing and more often associated with such cerebral applications as curing cancer or creating self-driving cars.
It is doing those things, with great fanfare in many cases, but the technology has a wide range of use cases, some of which are being applied in commercial real estate from valuation of buildings, to running smart offices to revenue management.
Chatbots, in other words, are just the start. In a way, they can be seen as the low hanging fruit. Page reports that RET Ventures has a couple of portfolio companies looking at expanding the chatbot’s typical use to other scenarios. “Existing tenants have questions too and the same tech can be applied to them. Perhaps they believe there is a leak in their bathroom. An AI-based chatbot can help them get to the bottom of that and schedule an appointment to fix it if necessary.”
Revenue management—how owners price apartment or housing units across portfolios—is another area investors are eyeing for AI, he continues.
There hasn’t been the same advancements in this area as the chatbots but Page reports that several companies are making progress on cracking this nut. “Legacy revenue management applications basically look at comparable buildings in order to price units but they do not get to the granular level, such as how to price a unit that looks out over an overpass, for example.” In other use, AI could conceivably process which businesses are nearby and which demographics would need to be targeted in order to maximize revenue for a particular building, he says. “Or you look at renewals and concessions and feed that data into a model. The system then tells you that this renter has a 90% chance of renewing so there is no need to reach out and offer a concession as his lease nears the end.”
There are few commercial applications on the market that do this now, Page says. He has heard of certain landlords, such as apartment owners and single-family rental home operators, developing such applications in house after hiring their own data scientists to lead such an initiative. These projects could go down one of two paths, Page says. Either they will be competitive intellectual property that the owner doesn’t want to share with a competitor or it will be productized and sold broadly.
One argument for the latter is that the more broadly a particular revenue management model is shared the more accurate it becomes. “We think eventually these companies will become comfortable spinning out the solutions they are developing in house,” Page says.

Some companies are not keeping their use of AI under wraps but instead openly incorporating it into pieces of its operations. Walker & Dunlop, for example, is using AI for property valuations and appraisals.  In 2019 it acquired Enodo, a tech startup focused on the underwriting process.  Then in January 2020, Walker & Dunlop and GeoPhy launched a joint venture called Apprise to focus on appraisals.
But even with whole divisions dedicated to adding automation and higher tech to underwriting and appraisals, experts find that tackling the tasks incrementally is still the best approach.
It is appealing to think that AI could automate the valuation of an entire building, says Marc Rutzen, CEO and SVP of Information Technology for Enodo. But many investors wouldn’t trust the end result of such a complex task, he says.
“We found it easier to apply and more readily accepted to use AI to handle smaller decisions in the underwriting process,” Rutzen says.
Consider the documents used in a valuation process—rent rolls and operating statements for instance. “These documents are completely different and in different formats. We build algorithms that pick out relative data, such as rents and unit numbers of the rent roll documents, and then run a series of predictive algorithms to see if the rents are too high or too low.”
Or the system could be used to see if expenses are historical outliers, Rutzen says. “We can point out to the underwriter that a certain expense is higher than it should be.” The system isn’t doing the job of the underwriter, he says. Rather it is helping him get to a final conclusion faster.
The value of the AI can be seen in the time savings for humans who still need to make final decisions. Before it deployed Enodo, Walker & Dunlop’s average time to process a rent roll was 55 minutes. Now it can process a rent roll in about five minutes, according to Rutzen. For operating statements, it took about 90 minutes to process current operations and four years of historic operations. Now it is down to 15 minutes.
“Multiply that by the thousands of deals we do and the thousands more we look at and that is a pretty substantial time savings in workflow automation,” Rutzen says.
Similar time savings also benefits Walker & Dunlop in the appraisal side of the business, says Meghan Czechowski, the valuation lead for Apprise and managing director of the firm’s midwest region. “Over the past four years the volume of multifamily transactions have spiked 74%, while the number of appraisers has decreased by 10%. The industry’s professionals are trying to keep up with ever escalating data, but they are doing it manually.” Applying AI to automate some of these processes can lead to valuable savings, she says.
The Apprise application pulls lead unit mix information from multiple sources into the system, and can compare these sources directly to map discrepancies and anomalies, she explains. “The constant updates for apartment unit mixes from industry standard resources coupled with our automated processing of actual rent roll information into unit mixes allows the system to record unit type and floor plan level changes over a period of time allowing appraisers a stronger base for trend analysis and support for rent forecasting within our appraisal reports.”
Apprise appraisers also use Enodo’s AI-based automated rent roll and operating statement parser, Czechowski says. “These features allow appraisers to process rent rolls into unit mixes and four-to-five operating statements in a matter of seconds, as opposed to manual processing which can take over one hour.”

AI is also being deployed in building operations. It harnesses a mix of Internet of Things, building data and information about tenants to manage energy consumption and operations based on the real time interactions of the building systems, Logical Buildings CEO Jeff Hendler explains. “It is a dance of machinery, humans, weather and market opportunity with the goal of reducing load,” he says.
One use case that is particularly current right now: office buildings in Manhattan are around 25%, if that, occupied by employees that have returned to work. Clearly, buildings’ systems need to adjust for this reduced workforce. “We have been able to provide property management a lot of insight in how to calibrate ventilation systems,” Hendler says.
How exactly does it do this? It is a mix, Hendler explains, of Logical Buildings’ digital data dictionary that it has built, coupled with its proprietary machine learning AI platform, SmartKit AI. These evaluate a wide range of factors from utility meter data, HVAC data, IoT data, outdoor temperature, time of day to utility tariff rates, to garner insights and provide guidance to lower building’s overall energy use and peak demand. “For example, the system may realize that when many people are out of the office for lunch, HVAC will not have to run with the same intensity to maintain a satisfactory comfort level,” Hendler says. “During the new normal of work from home, the platform has been invaluable to commercial owner-operators—to modulate building systems to minimize energy costs, as building space utilization fluctuates.”
The platform obtains information about building occupancy from door sensors and other IoT tools that detect movement, and, drawing upon its knowledge base regarding building operations, it will then flag if building systems seem to be working too hard, Hendler continues. “For example, if only 20% of workers are back in the office and temperatures are comfortable, energy use should be low. Assuming real-time utility readings are high, the system would then search for energy waste—for example, a compressor that does not have to be on given the current conditions—and alert the facilities team.”
Office buildings have primarily used this application to date, Hendler says, but with the spread of COVID  it has caught the attention of other asset classes, such as multifamily, to help control air flow within buildings for health reasons.
And in general, he adds, “It is a great opportunity for multifamily to know how to modulate building on stressful days.”

As such success stories become known, interest is bound to increase in AI-based commercial real estate applications. But, to state what is perhaps obvious, these systems are expensive and not always easily understood. If you are developing an app in house that almost certainly means you need a data scientist or several on staff and these people do not come cheap.
Any discussion of AI also needs to include a look at alternative legacy applications to see if these could do the job—perhaps not as well, but sufficient enough to solve a pain point.
“For many companies it comes down to that,” says RET Ventures’ Page. “Oftentimes they will conclude that they don’t need cutting edge technology even with all that it can accomplish. Yes, they may be excited about what could be accomplished five years from now with AI, but these companies may find it more urgent to solve a problem this year.”
Companies considering an investment in AI should ask themselves a few basic questions before taking such an expensive plunge, says Michael Yurushkin, CTO and founder of Brouton Lab, which has worked with several commercial real estate companies. “If you need AI only to analyze the data rather than letting AI take actions on your behalf, you’re better off sticking to the older technologies.”
Also key is whether you have the right data in place.
“For example, AI can analyze existing commercial real estate market data and decide which properties to recommend for brokers to buy or sell based on the broker’s past sales and intent,” he says. This might well be a wise investment.
“On the other hand, if you are missing a sufficient amount of high-quality data, AI will be useless for your business. Without data, it will not be able to forecast risk or to perform high accuracy predictive analytics. In this case, you are better off with older technology and systems that you are already using rather than investing a hefty amount in AI.”
After all, real estate still operates largely with manual processes and manual data collection, says Comly Wilson, director of marketing for Enertiv.
“There is plenty of room to run with technologies that don’t need the advanced pattern recognition capabilities of AI, he says. Building safety, for instance, does not necessarily require AI, he says. “A combination of air quality sensors, HVAC sensors and people flow sensors can tell operators the moment air flow or particulates in the air begin to cross unsafe thresholds. This data can populate a tenant-facing dashboard so they’re confident that ownership is being proactive. All that can be done without the need for AI.”