Use cases
Different goals, same workflow: explore, rank, filter, compare.
Some people are picking where to buy a house. Others are building a client shortlist or screening markets for an investment thesis. You bring the priorities - SkaldMaps brings the data and the map.
You set the criteria. We show the data. We don't tell anyone where they should or shouldn't live.
Home buyers
Find ZIPs you can afford where the schools beat the state average, the commute fits, and a hospital isn't an hour away. You set the priorities - we show the data.
“Where can my budget reach top-quartile schools without doubling my commute?”
- Find ZIPs you can afford where schools beat the state average
- Filter out places that look great until you see the commute
- Stop bookmarking towns at random - start with a ranked shortlist
Real estate investors
Find the right market before you go property-level. Screen down to the tract level by home price trends, rent benchmarks, and affordability ratios - and know where to look before you need MLS access.
“Which ZIPs or tracts show rent-to-value ratios above state median, home price appreciation over 4%, and income levels that support demand?”
- Screen all 33,000 ZIPs (or 85,000+ census tracts) by market signals you'd normally stitch together yourself
- Weigh appreciation, rent benchmarks, affordability, and access by your thesis
- Save one model per target area, run it across states
Realtors and agents
Before you pull comps, you need to know which market to pull them in. Use SkaldMaps to research where a relocation client should be looking - schools, ownership costs, weather, and access - without starting from a blank map.
“My client is relocating from out of state. Which three markets should we actually be exploring?”
- Research area fit before the MLS search starts
- Compare markets by the criteria your client actually cares about
- Show clients why you picked these areas - not just a gut feel
Curious researchers
Explore how schools, weather, broadband, affordability, and amenities cluster across the country - without stitching together a dozen data sources yourself.
“How are upper-scale grocery stores distributed once I filter by income, commute, and broadband context?”
- Browse the country through layered datasets
- Inspect how signals cluster across metros and states
- Move from curiosity to a saved ranking model
Ready to try it