In light of recent Covid-19 events we are summarizing the ways machine learning and AI can help real estate industry handle the changes, weather the storm, and take advantage of new opportunities.
Machine Learning Use Case 1: Lead Generation / Prospecting
Primary User: Commercial Real Estate Brokerage, Lenders, Acquisition Teams
Increased performance efficiency via business intelligence reports;
Tracking of market and competitors for sales, prospecting, fundraising, business development;
Automation of manual tracking of industry, competition, market.
Traditional real estate prospect marketing involves utilizing existing network and posting property listings or leasing opportunities on marketplaces. At first, it’s effective, but networks are limited and the market potential gets tapped out. In addition, junior brokers have to manually track active real estate investors to approach by reading industry press and constantly updating prospects information in Excel. This activity is highly reactive and not particularly insightful. By analyzing the market infrastructure and macroeconomic data from heterogeneous sources predictive models can be developed. These models will cut marketing costs by generating lists of potentially undervalued properties, automatically scoring prospects, and directly uploading the information via an API into the client’s’ Enterprise Resource Planning (ERP) and/or Customer Relationship Management (CRM) systems. This process significantly cuts time, cost and errors and increases revenue generation by efficiently targeting the most valuable prospects.
This approach is specially designed to help brokers and fundraisers research and identify key prospects that are more likely to partner on listings and deals.
It can be utilized for:
• Lead generation: identify geo area of interest and a targeted transaction and the model will produce a limited list of buildings/prospects that are undervalued/overpriced in that particular region.
• Lead qualification: list prospects most likely to submit offer/to buy (highest purchasing intent); from the list of purchase offers identify the one most likely to close; properties and firms likely to dispose properties (based on their past market activity).
Example of the prospecting model:
Example of the CRM with real-time info on potential customers:
Integrating data mining, machine learning, and semantic indexing from multiple databases such as CB Databank, Pitchbook, Crunchbase and scoring potential leads according to specific scenarios would result in the list of the most probable buyers or equity investors.
Some ideas for prospecting scenarios to build into the model might be:
- Balloon loan maturing in 6 to 9 months;
- Credit upgrade or downgrade on a major tenant,
- Tenant’s recent fundraising event or hiring announcement;
- Tenant’s in the hard-hit sectors (e.g. restaurants without outside dining area);
- Certain macro factor drastically changed in that particular MSA (beware of the reporting time lag, though)
Another approach would be to scour the news for a particular metric, such as acquisitions, disposition, financing and investment announcements. Plenty of start ups offer New Analytics tools to build a news tracking prospecting model. Check out Aylien or YUKKA Lab.
Example of the news tracking prospecting model:
Some of the Capital Brain lender clients reported success in their prospecting efforts by tracking public data from SEC private placements filings. CB machine learning algorithm reconciles obscure LLC names and the sponsors behind the funds. Lenders would immediately get a list of firms that are in the fundraising mode and might be looking for financing on their future acquisitions.
Example of the Private Placements prospecting model
These are just 4 typical use cases for prospecting, acquisitions, and dispositions but they can potentially eliminate manual data tracking on existing prospects and efficiently build highly targeted prospect lists.
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