Identifying property location or ownership information using Assessor-collected data are just two basic problems you can solve with parcel boundaries. But there are many higher-level analyses that can be done using spatial data. We hinted at this in a previous blog when we discussed the retail and restaurant site location use case, specifically using multiple data layers on a map, not just parcel boundaries linked to public record data. Spatial data itself can be used to derive other data and can also tell time.
Here are just 3 examples of spatial analysis and the kinds of complex problems they can solve.
Example 1: Solar Panel Companies
How could a solar panel company determine who is a good candidate for purchasing their product? Are the houses facing the correct direction to take advantage of this eco-friendly upgrade? Can the homeowner take advantage of special financing options? Identifying structures that are South and West facing is key to finding properties suitable for solar panel installation. With a combination of parcel boundaries and building footprints, it’s possible to determine the direction a house faces.
First American Data & Analytics happens to have done this analysis already.
Finding the right customer is not just about the property. There is also the factor of cost. Solar panel installation is a large budget item, so it’s necessary to know which homeowners have enough equity or can access Property Assessed Clean Energy (PACE) funding. Equity is calculated using the estimated value of a home and the estimated outstanding loan balance. First American Data & Analytics offers both data elements in addition to pre-calculated equity. Of course, it’s also important to know which properties already have solar panels. PACE data can provide that insight, too.
Example 2: Homeownership Assistance
What if you needed to track the success of statewide programs designed to increase homeownership such as downpayment assistance, shared equity, or reduced interest rates? How would you see what regions benefited from assisting low- and moderate-income renters become homeowners? To answer these questions the properties that received assistance can be plotted on a map with different layers reflecting home purchases in different years. These data points show how areas around the properties purchased in the first year of the data expand to include other properties in subsequent years. Add in other properties purchased with alternate assistance (e.g. FHA loans, county or city housing assistance programs), property value changes over that same time period using an Automated Valuation Model (AVM), and the properties facing foreclosure to get a broader view of the local housing economy.
Example 3: Investor Properties
Why should an investor purchase a property in an up-and-coming neighborhood? Investors want to find properties with a lot of upside potential but minimize the chance of their asset losing value.
Detroit is an example of a city that has fallen on hard times. Some areas, however, are on the rise, and they can be discovered in our DataTree solution using vacancy flags, listing and AVM prices, and both parcel and neighborhood boundaries. Untapped potential can be found by analyzing the difference between listing price and the estimated value of surrounding properties. Plotting where the vacant properties are in different neighborhoods exposes risk for investing in that neighborhood. Showing where neighborhoods are in relation to each other demonstrates where there are economies of scale for investors looking to make a bigger impact on an area. All this analysis lit up on a map makes choosing properties to buy much easier.
Spatial data is very powerful. It not only brings to life simple insights, but also allows for robust analysis to reveal where, how, and why your business can grow. First American Data & Analytics is focused on offering the most accurate parcel boundaries in the industry so that our customers can make confident business decisions.
If you don’t know in what direction you want to go or even what direction is possible, we’re here to help. We thrive on opportunities to share our spatial knowledge, excitement, and vision, as well as our data, to present our customers with possibilities that bring the future a little closer.
Have questions about our parcel boundary data? Reach out to us at datadriven@firstam.com.