Translating your world, one neighborhood at a time
User location shows all neighborhoods in a city
Explore every neighborhood via key characteristics
Compare to any city or the national averages
Users identify a current location, neighborhood name, or address
Based on analysis of over tens of thousands of different data points, will alert users to neighborhoods that have the same “Look and Feel”
Users will tap a neighborhood, and MapVida will provide key details and allow comparisons to known areas
Users can build their ideal neighborhood, based on key categories
MapVida will identify all neighborhoods that fit the users requirements
Users are able to remap an unknown city, and its neighborhoods, into familiar ones
Color code the different neighborhoods to see similar areas throughout the city
Tap neighborhood to see important details, comparison to familiar neighborhoods
Quickly put a new city or area into a familiar context, depending on how long you plan on living there (1 weekend, 1 year, 10 years)
Explore a new city, but in familiar context. Allows you to “live like a local.”
Promote “hidden” inventory in areas not linked geographically, but share similar “look and feel.” Achieve better view on pricing models, based on consumer neighborhood preferences
Use consumer preference and current location performance to focus on development areas and asset management
Who lives there (e.g., married vs. single, commuters vs. walkers etc.)
Real estate types (e.g., single family/Multifamily mix, home sizes, age of properties, rent amounts, housing prices)
Businesses in the area (e.g., retail types, numbers, restaurant types, and employer size)
Macro and infrastructure (e.g., crime data, school quality, consumer spending, transportation)
Geospatial and environmental (e.g., parks, water, hiking, weather)
Trending data over 15 years (e.g., how much has the area changed, “from auto shops to coffee shops”)
Mike’s professional experience has two anchors: predictive analytics and real estate technology. Mike has held leadership and product development positions for risk and data analytic providers to the multifamily and mortgage industries. He is the co-inventor on multiple issued patents (focused on predictive analytics, risk assessment, and e-commerce). In his free time, Mike enjoys watching Minnesota’s professional sports teams lose in heart-breaking fashion.
Steve has held revenue and operational leadership positions with risk and analytic software companies focused on the real estate industries. Steve is also an active investor and advisor to multiple technology startups. He is the co-inventor on multiple pending patents (focused on predictive analytics). In his free time, Steve likes to tease Mike Mauseth because Mike thinks Notre Dame stole Lou Holtz away from the University of Minnesota.
Jason is the architect of several successful technologies used throughout the multifamily and mortgage industries. He has worked with cross-functional teams from planning through release to meet customer needs and expectations. Though extremely technical, Jason bleeds user experience and ease-of-use (which, ironically, makes donating blood difficult). In his free time, Jason enjoys watching Arsenal soccer and wondering if Mike should take up quidditch instead of Minnesota football.
Scott has spent over 25 years in commercial real estate — delivering data and analytic software solutions to the multifamily and single family industries. As a senior sales executive, Scott has led several successful product rollouts for large multinational corporations — developing product and channel strategies that deliver real value to real estate operators and marketers. Scott is at his “sales” best when building both business and personal relationships that translate into long term client satisfaction. In his spare time, he enjoys the social aspect of college football (GO DAWGS!!!) and is now a Minnesota Vikings fan at the insistence of Mike Mauseth (misery loves company).