Helping clients use data as a primary scaffolding to real estate business decisions and technology platforms
The real estate market is overflowing with rich data opportunities. Consumers are data-producing machines. Every day, potential renters and homebuyers leave an abundance of online breadcrumbs that signal their buying or renting intent. This data can be deployed to develop predictive behavioral models.
Before a property is built, construction companies must partner with developers to build a property in a chosen parcel of land, either for residential, business, or mixed-use. Property development is contingent upon maximizing cashflows. Profits are only realized when tenants fill rooms. Artificial intelligence’s power is it’s ability to forecast and plan future variance in cashflows.
Predictive modeling and customer behavior profiling can analyze demographic, lifestyle, spending pattern, socioeconomic, language use, and crime data a particular area to predict the most desirable customer traits. A developer can devise an optimal mix of products and services for a particular location and glean insightful information to simplify customer acquisition.
Analytics can make securing successful site locations easy. Understanding a neighborhood’s consumer base, defining a realistic sales goals for a particular area, determining the viability of expanding a store branch or restaurant into a neighbor is a tricky proposition. Site selection and portfolio management models utilizing flood maps, demographics reports, EPA reports, traffic counts, and any other economic data provide potential buyers with an accurate estimate of IRR and NPV.
Standard analytics lets real estate professionals measure the performance of their website traffic and marketing initiatives. Website analytics provides a company with details such as the number of site visitors, or where individuals lose interest. Understanding which pages drive the most traffic, generate the most referrals, or get the most social shares is priceless information to real estate marketing departments who spend small fortunes in advertising.
Predictive analytics can help decision makers understand a property’s ROI. Sellers who want to know how much a kitchen or bathroom renovation will affect a home’s value can utilize analytics to predict the value of upgrades by cross-referencing other similar neighborhoods. Agents can recommend specific home upgrades and/or renovations that allow sellers to earn more at closing. These insights can allows for better pricing and prevents listings from going “stale”.
Utilizing nontraditional data, machine learning models have been able to predict rent prices at an accuracy of 90%. Variables related to traditional data sources, such as vacancy rates, correlate well with future home values, but nontraditional variables data, like proximity to highly rated restaurants or lowering vacancy rates for nearby apparel stores, explained 60% of the prices changes in rent.
of real estate agents had a 94% higher chance of winning a potential listing they target with AI than not.
2021 CNBC. “Artificial Intelligence Is Taking over Real Estate – Here’s What That Means for Homebuyers,”
State business metrics
Summarizing past events such as sales data from marketing campaigns
Tracking detailed website activity
Tracking social media usage and engagement data
Collating survey results
Understand and exploit clickstream patterns
Analyze employees’ performances from their trackable behavior
Classify different prospects into groups
Determine causal relationships between different data points
Forecasting product returns
Predictive asset maintenance
Predicting credit default
Dynamic or surge pricing
Optimize marketing strategies
Minimizing energy usage through better route planning
Tracking historical trading data to measure trade today’s financial risk
PRAM Insurance Services
Senior consultants with previous experience with these types of projects can set the stage for a well-framed engagement.
A focused session on your specific software applications, platforms, or projects. Typically this includes technical resources from both sides.