Appraisals Are Broken. Machine Learning Can Fix Them.

Our near and dear old-economy business of real estate - which makes up more than 10% of the nation’s GDP and $32 trillion (!) of value globally - has plenty of entrenched norms and processes that technology could help make more efficient. Appraisals - especially the analysis behind them - are one such corner of the industry. Their weight is hard to overstate: whenever an asset is financed or an owner needs to value an asset or portfolio without selling, owners turn to appraisers. Regulators and insurers lean on them too. Everyone knows the appraisal process is flawed but recent research confirms that a dose of machine learning could make appraisals materially more accurate.¹

Specifically, using machine learning algorithms improves appraisal accuracy by 20% for apartments, 18% for industrial, 16% for office and 14% for retail assets. Also, the dispersion of ML-derived values around the actual sale value is tighter than that of traditional appraisals, and while traditional appraisals systematically overvalue assets, the ML valuations were not biased in either direction. This is better in so many ways.