August 31, 2023
of CRE firms employ AI and machine learning to support decision-making
of CRE firms plan to invest in AI and ML capabilities in the next 2-3 years
of CRE firms are investing in ML, data science, and related tech to gain a competitive edge
Scheme title: Real estate disruptors by importance
Data source: pwc.com — Emerging trends in real estate 2023
Nowadays, AI-based technologies are a staple for most real estate platforms, apps, and services, supporting most corporate functions and business processes in this industry.
ML-based recommender systems implemented on major real estate platforms provide customers with tailored suggestions based on past online interactions, including search history. Similar to their counterparts deployed in retail, the aim of these engines is to narrow down property search and route users to assets matching their needs and requirements.
Coupled with IoT devices, ML solutions for anomaly detection can monitor key components of a property, including the electrical system, to identify any outliers, forecast failures, and enable predictive maintenance. These tools also optimize power consumption by spotting any shift from ideal conditions, such as suspicious spikes in energy usage.
Real estate enterprises can leverage ML to safeguard their businesses from fraudulent activities and streamline KYC procedures. Powered by machine learning algorithms, fraud detection systems cross-check financial transactions, criminal records, creditworthiness, and other data on potential partners, tenants, or customers to identify inconsistencies.
Enhanced with AI-based NLP capabilities and superior context understanding, RPA bots in real estate can replace or assist managers and accountants in performing a wide range of clerical tasks. Automatable processes include tax billing and reporting, lease contract processing, tenant onboarding, bank reconciliation, AR/AP, and NAV calculations.
Redfin implemented an ML-powered real estate appraisal system enabling users to estimate the market value of their properties based on over 500 metrics, including buyer demand and neighboring home prices. The tool estimates around 92 million properties across the US, showing a valuation accuracy of 98% for on-market homes and 93% for off-market assets.
The path to implementing machine learning can vary depending on the use case and the type of solution selected. However, these are some typical steps you may go through.
Initial data analysis
Building the ML solution
Model integration and deployment
The coding phase encompasses front-end and back-end development, followed by embedding the ML model into the software architecture to enable its AI-powered functionalities. You should also configure all necessary API- or ESB-based integrations to fuel your solution with data from other applications and IoT sensors. Cloud data integration tools can help in this regard. Then, you can deploy the ML solution on-premise or in a cloud-based environment.
ML-powered cognitive technologies like NLP and computer vision can harvest real estate and market data from multiple sources, providing analysts with a wider set of real estate-related metrics to better interpret trends or phenomena.
According to Forbes, ML-powered Automated Valuation Models (AVM) can assess the value of an asset with an absolute error of less than 4% for homes and under 6% for commercial properties, offering accurate insights for better investment decisions.
ML solutions encompass powerful tools, such as recommendation engines, to route customers toward suitable listings, along with features and techniques to drive sales, including user segmentation, targeted advertising, and lead scoring.
From the automation of time-consuming clerical processes via software bots to power consumption monitoring through data analytics platforms, machine learning solutions help real estate organizations to optimize their budgets and resources.
Thanks to machine learning, your customers and tenants can benefit from instant and convenient support via chatbots and virtual assistants. Furthermore, real estate platform users can enjoy easier property searching, tailored recommendations, and virtual tours.
ML systems' anomaly detection and predictive analytics capabilities safeguard your business from various financial risks, including fraud and market volatility. They also help ensure safer living environments through predictive maintenance.
While machine learning benefits your business, implementing it can involve various business and technical complexities. Here are some tips to overcome typical obstacles and streamline ML adoption.
A common obstacle to machine learning implementation you can face in the early assessment stage involves selecting a use case where it makes sense to opt for a ML solution over more conventional technologies. In this regard, O'Reilly’s 2020 survey ranks use case identification second among the major AI adoption challenges. Still, this choice is crucial, as it will determine the ROI of your ML project and impact executives’ and stakeholders’ buy-in.
Given ML's requirements in terms of model training, architecture, and processing power, solutions based on this technology can be more costly than "standard" software. So, it's worth investing in ML to enhance the most impactful and profitable business domains in your industry or key corporate functions suffering from major inefficiencies. According to Altus Group, for instance, most real estate firms adopt ML to target markets and assets for investments, streamline internal operations, enhance portfolio management, facilitate property valuation, and select partners.
Scheme title: Top reasons CRE firms are investing in ML and data science
Data source: Altus Group — The state of data science in commercial real estate investing
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