Machine augmented residential underwriting at scale for the urban core, with human interaction pushed out to the edge.
ReAlpha uses primary structured real estate data, and layers in numerous sources of unstructured ancillary and derivative data to manufacture “alpha” in making real estate investment decisions. Urban residential markets are incredibly opaque, and transactions are consummated on direct negotiations with imperfect and asymmetric information. ReAlpha uses machine learning and its proprietary algorithm to identify mispriced units, generate recommended buy/sell points, and create portfolios optimized for return while reducing expected volatility.