1
$\begingroup$

I am working on optimising a real estate portfolio.

I have a total returns series for various property types and locations.

I am comfortable with my expected returns estimates, but I have significant concerns with the estimates on risk. The series is more of a market average, which provides a false sense of security as to how volatile the asset is. Also, the historical data is often patchy and looking at volatility alone does not provide sensible results.

I have been reading around but haven't had much luck here.

Are there methods to help me to choose my own risk measures? I would like to add penalties for certain asset classes and locations which clearly aren't being penalised enough in my current models.

I have been working on this in R.

$\endgroup$
3
$\begingroup$

This is language and asset class-agnostic, and applies to any quadratic optimization.

There are two approaches.

  • impose linear constraints for every location and every class. (I guess with real estate, you could say something like, no more than 10% in Las Vegas, and no more than 15% in shopping malls. And, of course, no more than n% in any single investment.)

  • Assign some undesirability score to each candidate (e.g. 0 - you're not concerned, 1 - you're concerned a little, 2 - you're concerned a lot) and include it (multiplied by the weight) in the objective function being niminized. (Be sure to scale it so it affects the optimal portfolio, but does not overwhelm the minimum-covariance part of the objective function.) And/or, impose a linear constraint on the total undesirability score of the portfolio.

$\endgroup$
1
  • $\begingroup$ Should I go with the second option, would that be a factor-based constraint? $\endgroup$
    – Jamzy
    Sep 28 '20 at 0:50

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.