Amid rising opportunities in the single-family rental (SFR) market, a new breed of geospatial property analytics is dramatically improving the accuracy and utility of automated valuation models (AVMs) for tech-savvy SFR investors.
Even before COVID-19, demographic trends and an intractable housing crunch were making SFRs one of the hottest asset classes in real estate investment.
What is an SFR property?
SFR stands for “single-family rental” — your typical standalone home — and SFR investing consists of individual and institutional investors who purchase single-family homes and then rent them out to produce cash flow.
Trends in SFR Investment
The Urban Institute estimates that rental growth will continue at twice the pace of homeowner growth through 2040, and the pandemic only amplified this trend. Within the first six months after remote working measures were put in place, as many as 15.9 million people moved out of cramped, costly metros—with most favoring rentals in nearby, and sometimes quite distant, locales.
But as Globest reports, the same pandemic behind the spike in rental demand has also resulted in massive job losses, lower rent prices, and eviction moratoriums that have reduced on-time rental payments and put the crunch on smaller investors. According to RealtyTrac, single-family rental property owners in 48% of all US counties are at above-average risk for default.
What this means to the market remains to be seen. But even if outcomes include increased inventories, it will just complicate decision-making in what is already an increasingly competitive market. With a growing number of major investors and asset managers racing to snatch up the choicest properties in the most resilient neighborhoods and markets, fast and accurate valuation is critical to success.
But that means one source of intelligence used for valuing properties—the AVM—may not cut it on its own anymore. Let’s look at why, and how geospatial property analytics are changing the equation.
Standard AVMs: Missing The Condition Factor
The events of 2020 and the need for pandemic-induced distancing measures have increased the role AVMs play in assessing and valuing properties.
Generally speaking, AVMs are tools that provide real estate property valuations using mathematical modeling based on comparable properties, tax assessments of value, and in some cases, factors such as historical house price movements, and surveyor analysis. All delivered in a matter of seconds. But all using data that can be out of date by a year or much more and assuming average condition for a property.
In a different era, when investing in SFRs was primarily a local affair, filling in that kind of information gap might have been somewhat straightforward. But as new technologies enable larger real estate investors to easily manage thousands or tens of thousands of properties at once, bigger players and billions of dollars in capital are pouring into the sector.
Because AVMs miss critical and current property features and conditions that impact value, their usefulness in the new world of SFR investing is limited. But as new initiatives are proving, augmenting AVMs with geospatial property analytics can fill in gaps and errors quickly and cost effectively, and at the scale required for today’s investing environment.
So What Are Geospatial Property Analytics?
Geospatial property analytics enable the automated assessment of properties and property conditions using artificial intelligence and imagery captured from planes and satellites.
Powered by AI, these solutions leverage high-resolution aerial imagery and computer vision to extract structured data from the visuals. This includes details such as the type of roof construction and its condition, the presence of solar panels, whether there are overhanging trees, the overall size of the property, and whether the property has a pool, yard debris, and more.
This information can help SFR investors gain a high-fidelity understanding of property conditions so they can make informed, real-time transaction decisions.
When integrated with existing workflows through an API, this kind of property data can enhance AVMs with unique insights that provide considerable lift to valuation, reduce outlier home price predictions, and help produce more accurate models. They’ve been shown to dramatically improve the predictive value of internal analytics as well.
Here are a few things to keep in mind when working with geospatial analytics.
AI in the Sky: Geospatial Property Condition Assessments, at Scale
Advanced geospatial property analytics solutions leverage high-res aerial imagery and can provide instant access to tens of millions of properties nationwide. That includes Cape Analytics’ own solution, which is able to remotely and instantly gather property condition data on tens of millions of SFRs across the US—delivered with the speed and coverage of traditional property record data.
Here’s why that matters. Whether investors are exploring a single property, or shopping citywide, regionally, or even nationally, they can instantly access neighborhood, census tract, or zip code, or CBSA data so they can zero in on the most promising regions and properties. They can also review aggregate changes over time to unearth trends (both good and bad) that might otherwise be extremely hard, if not impossible, to recognize.
It’s easy to see how this kind of intel could dramatically increase AVMs’ utility and accuracy in property valuation and selection so SFR investors can make smarter, faster decisions and stay ahead of increasingly formidable competition.
In fact, it’s already in use today.
Enter: The Modern, Geospatial-Enhanced AVM
Together with Weiss Analytics, Cape Analytics is powering ValPro+, the first automated home valuation engine that leverages geospatial imagery and AI to integrate current home conditions into valuations.
“One of the key missing ingredients which this now answers is, how do you know what the condition of the house is?” Allan Weiss, head of Weiss Analytics and co-creator of the Case-Shiller Index, tells HousingWire. “We didn’t know things like that before. We didn’t know what it meant if there was a lot of yard debris, or there was a lot of vegetation overgrowth, or what it meant if the condition of the roof was very good versus very bad in a given market.”
So far, the engine has shown a 7.7% improvement in PPE10 predictions of on-market valuations. Given the risks they’ve got to navigate this year, don’t be surprised if SFR investors start hunting for AVMs enhanced with these kinds of high-quality geospatial property analytics—or end up wishing they had.
To learn more about how geospatial property analytics solutions from Cape Analytics can enhance your SFR investment strategies, click here.