This study aims to estimate the market risk of private real estate assets, specifically examining Swedish real estate companies, and seeks to identify the best model for estimating the quarterly squared return. An important assumption in this study is that private real estate assets are assumed to have the same market risk as publicly traded assets, all else being equal. With this assumption, the studied methods can be applied to publicly traded companies and evaluated based on the realized stock returns of these traded companies.  The study examines two primary techniques for estimating the risk of private real estate assets: desmoothing of appraisal based returns and supervised learning on listed peers. Desmoothing is a technique used to estimate new economic returns from smoothed real estate appraisal returns. The original desmoothing method outlined by Geltner (1991) introduces AR desmoothing and is examined along with the MA desmoothing model presented by Getmansky et al. (2004). Performing these desmoothing techniques yields a new time series of returns that can be utilized in an EWMA (Exponentially Weighted Moving Average) estimation for predicting the squared return of the next quarter. The supervised learning on listed peers, on the other hand, is performed by studying similar listed assets and training the ability to predict the squared return based on explanatory variables representing selected key figures of the companies’ financials. Five supervised learning models are examined: Linear Regression, Lasso Regression, Ridge Regression, Elastic Net Regularization, and Random Forest Regression.  The results show that four out of the five supervised learning models are superior to the desmoothing models. In particular, Random Forest Regression, Ridge Regression, and Lasso Regression yield the best estimates of the quarterly squared return. However, since this study assesses risk over a quarterly time period, the lack of data is significant, affecting the statistical confidence of the results.  Although the superiority of the supervised learning models in terms of predicting the squared return is evident, the results from the desmoothing reveal some interesting properties about the techniques. AR desmoothing reduces the disparity between the sample variance of the stock compared to the original NAV time series, whereas MA desmoothing drastically increases the correlation of the desmoothed returns with the stock returns.