I compare the performance of the index‐based time series approach and the cross‐sectional approach in estimating factor loadings of nontraded assets, and show that the latter likely provides less biased and more efficient estimates. I then use the cross‐sectional approach to estimate the loadings of privately owned commercial real estate on the Fama and French (1993) factors, the Pastor and Stambaugh (2003) liquidity factor, and two bond market factors, using a sample of 14,115 properties in the 1977–2012 period. I find statistically significant loadings, of which the signs seem consistent across property types, but the magnitude varies. Using the time series approach on the same data, I find insignificant loadings on virtually all factors. To investigate the sources of the weak results from the time series approach, I conduct a Monte Carlo simulation in which both approaches are correctly specified and indices can be estimated perfectly. Simulation results suggest that the cross‐sectional approach provides more accurate estimates under reasonable market conditions.