Big Data in Real Estate? From Manual Appraisal to Automated Valuation

Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. This paper provides a practical application of “big data,” employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. We find strong evidence on the superiority of automated valuation models (AVM) over traditional appraisals – the absolute error of the automated model is 9 percent, which compares favourably against the accuracy of traditional appraisals, while the model can produce an instant value at every moment in time, at a very low cost. We also provide evidence on the importance of using “hyperlocal” information on the location of an asset. The model developed in this paper is directly applicable for real estate lenders and investors, and has important implications for the traditional appraisal industry.Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. This paper provides a practical application of “big data,” employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. We find strong evidence on the superiority of automated valuation models (AVM) over traditional appraisals – the absolute error of the automated model is 9 percent, which compares favourably against the accuracy of traditional appraisals, while the model can produce an instant value at every moment in time, at a very low cost. We also provide evidence on the importance of using “hyperlocal” information on the location of an asset. The model developed in this paper is directly applicable for real estate lenders and investors, and has important implications for the traditional appraisal industry.