CAPE Analytics Announces Launch of First-of-its-Kind Liquidity Score to Predict How 'Sellable" A House Is

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Today, CAPE Analytics, the leader in AI-powered geospatial property intelligence, announced the launch of the CAPE Liquidity Score, an advanced first-of-its-kind machine learning model used to predict property liquidity. Namely, whether a property is likely to be sold faster or slower than other homes in its neighboring community and price range, without needing to discount its price. 

As interest rates begin to rise, refinances are drying up, fewer homes are being put on-market, and home purchases may reduce as fewer buyers qualify. As it stands currently, home prices are up almost 20 percent year-over-year, and mortgage rates have risen dramatically. The rate for a 30-year fixed loan is now 5.57 percent, up from 3.29 percent at the start of 2022. CAPE Liquidity Score provides great utility in this increasingly uncertain environment by acting as a risk assessment tool for loans and real estate transactions.  

Despite the importance of liquidity in measuring property desirability and loan risk, there has yet to be a solution that is capable of both accurately and efficiently predicting the liquidity of a property. CAPE Liquidity Score serves to address this solution by using machine learning, geospatial data, and high-resolution imagery to analyze more than 40 property attributes that are predictive of actual home sales activity. 

Liquidity has long been a concept that institutional real estate investors and loan originators have wanted to account for to better understand how a home might behave when it is placed on-market to be sold. However, due to a lack of understanding around the necessary details of a property including its condition and geospatial factors, automated valuation models (AVMs), mortgage originators, and HELOC originators have been unable to harness this concept into a useful, usable data point that supports decision making. CAPE's Liquidity Score is the first model of its kind that can accurately predict how a given home will perform on the market, thereby allowing these parties to reduce errors in valuation, prepayment risk and default risk.  

“While the real estate industry has always had some intuition into the concept of liquidity, it has lacked the ability to properly measure and predict it for a specified property,” said Raj Dosaj, Head of Real Estate at CAPE Analytics. “Predicting liquidity was previously impossible without adequate remote and recent property intelligence. CAPE's underlying property attributes are predictive of liquidity, thereby introducing insight into home salability.” 

CAPE Liquidity Score provides investors in single-family real estate and mortgages with a direct method to assess the marketability of a property that serves as the basis for investment valuation and collateral risk unlike other measures on the market today. It is a critical input into determining the value of a home and a mortgage's future likelihood of prepayment, default, and loss. 

The announcement comes on the heels of the launch of CAPE's AIRE platform which combines computer vision, aerial imagery, and proprietary data sources to extract critical information about properties, focusing on characteristics that influence their value and marketability. 

For more information about CAPE's Liquidity Score, visit www.capeanalytics.com 

About CAPE Analytics

CAPE Analytics provides instant property intelligence for buildings across the United States and Canada. CAPE Analytics enables residential and commercial property stakeholders to access valuable property attributes instantly, with the accuracy and detail that typically requires an on-site inspection, but with unparalleled immediacy and global scale. Founded in 2014, CAPE Analytics is backed by leading venture capital firms and insurers and is comprised of machine learning, data infrastructure, and property risk experts. CAPE Analytics was recently voted San Francisco Business Times' No. 1 Best Place to Work in the Bay Area.

— WebWireID290543 —


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